Search

Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced interaction clue and produce suboptimal representations to users and items for recommendation. Towards the issue, this work is dedicated to bias-aware embedding learning in a decomposed manner and proposes a counterfactual graph convolutional learning (CGCL) model for personalized recommendation. Instead of debiasing with uniform interaction sampling, we follow the natural interaction bias to model users' interests with a counterfactual hypothesis. CGCL introduces bias-aware counterfactual masking on interactions to distinguish the effects between majority and minority causes on the counterfactual gap. It forms multiple counterfactual worlds to extract users' interests in minority causes compared to the factual world. Concretely, users and items are represented with a causal decomposed embedding of majority and minority interests for recommendation. Experiments show that the proposed CGCL is superior to the state-of-the-art baselines. The performance illustrates the rationality of the counterfactual hypothesis in bias-aware embedding learning for personalized recommendation. [ABSTRACT FROM AUTHOR]

In today's digital landscape, recommender systems have gained ubiquity as a means of directing users toward personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently the popularity bias. This bias hampers the diversity of recommended items, thus restricting users' exposure to less popular or niche content. Furthermore, this issue is compounded when multiple stakeholders are considered, requiring the balance of multiple, potentially conflicting objectives. In this article, we present a new approach to address a wide range of undesired consequences in recommender systems that involve various stakeholders. Instead of adopting a consequentialist perspective that aims to mitigate the repercussions of a recommendation policy, we propose a deontological approach centered around a minimal set of ethical principles. More precisely, we introduce two distinct principles aimed at avoiding overconfidence in predictions and accurately modeling the genuine interests of users. The proposed approach circumvents the need for defining a multi-objective system, which has been identified as one of the main limitations when developing complex recommenders. Through extensive experimentation, we show the efficacy of our approach in mitigating the adverse impact of the recommender from both user and item perspectives, ultimately enhancing various beyond accuracy metrics. This study underscores the significance of responsible and equitable recommendations and proposes a strategy that can be easily deployed in real-world scenarios. [ABSTRACT FROM AUTHOR]

Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/Deep-Deconf. [ABSTRACT FROM AUTHOR]

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics. [ABSTRACT FROM AUTHOR]

Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, at both the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems. [ABSTRACT FROM AUTHOR]

Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks quality measures that effectively enable their analyses and comparisons. In this article, we first introduce a formal, data-driven, and parameter-free strategy for classifying items into low, medium, and high popularity categories. Then we introduce Balanced Quality Score (BQS), a quality measure that rewards the debiasing techniques that successfully push a recommender system to suggest niche items, without losing points in its predictive capability in terms of global accuracy. We conduct tests of BQS on three distinct baseline collaborative filtering frameworks: one based on history-embedding and two on user/item-embedding modeling. These evaluations are performed on multiple benchmark datasets and against various state-of-the-art competitors, demonstrating the effectiveness of BQS. [ABSTRACT FROM AUTHOR]

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g., by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority: a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy: a previously-proposed fairness notion that quantifies the extent to which a user prefers other users' recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility: the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state-of-the-art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible. [ABSTRACT FROM AUTHOR]

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user's preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user's long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]

Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today's cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item metadata, and knowledge graphs will likely result in a poorly performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges—handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity—we designed a new end-to-end deep Adversarial Multi-channel Transfer network for Cross-Domain Recommendation named AMT-CDR. Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs—we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. Data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR. [ABSTRACT FROM AUTHOR]

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user's feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance. [ABSTRACT FROM AUTHOR]

Privacy and trust are highly demanding in practical recommendation engines. Although Federated Learning (FL) has significantly addressed privacy concerns, commercial operators are still worried about several technical challenges while bringing FL into production. In addition, classical FL has several intrinsic operational limitations such as single-point failure, data and model tampering, and heterogenic clients participating in the FL process. To address these challenges in practical recommenders, we propose a responsible recommendation generation framework based on blockchain-empowered asynchronous FL that can be adopted for any model-based recommender system. In standard FL settings, we build an additional aggregation layer in which multiple trusted nodes guided by a mediator component perform gradient aggregation to achieve an optimal model locally in a parallel fashion. The mediator partitions users into K clusters, and each cluster is represented by a cluster head. Once a cluster gets semi-global convergence, the cluster head transmits model gradients to the FL server for global aggregation. In addition the trusted cluster heads are responsible to submit the converged semi-global model to a blockchain to ensure tamper resilience. In our settings, an additional mediator component works like an independent observer that monitors the performance of each cluster head, updates a reward score, and records it into a digital ledger. Finally, evaluation results on three diversified benchmarks illustrate that the recommendation performance on selected measures is considerably comparable with the standard and federated version of a well-known neural collaborative filtering recommender. [ABSTRACT FROM AUTHOR]

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow, and the research must consider other important factors, particularly trustworthiness. A trustworthy recommender system (TRS) should not only be accurate but also transparent, unbiased, fair, and robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast-developing field of TRSs. To this end, in this article, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs. [ABSTRACT FROM AUTHOR]

Recommender system helps address information overload problem and satisfy consumers' personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players' preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network–based method is designed to model players' multi-source heterogeneous information, including the players' historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers. [ABSTRACT FROM AUTHOR]

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users' privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users' privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users' preferences but do not reveal their private information, and then we design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused by historical preferences of uploaders. Since historical preferences are not fully reliable and may reflect obsolete behaviors, over-reliance on them should be avoided as knowledge and interests dynamically evolve. In this article, we propose a Deconfounded Cross-Modal matching model to mitigate such bias. Specifically, uploaders' personal preferences of music genres are identified as confounders that spuriously correlate music embeddings and background music selections, causing the learned system to over-recommend music from majority groups. To resolve such confounders, backdoor adjustment is utilized to deconfound the spurious correlation between music embeddings and prediction scores. We further utilize Monte Carlo estimator with batch-level average as the approximations to avoid integrating the entire confounder space calculated by the adjustment. Furthermore, we design a teacher–student network to utilize the matching of music videos, which is professionally generated content (PGC) with specialized matching, to better recommend content-matching background music. The PGC data are modeled by a teacher network to guide the matching of uploader-selected UGC data of student network by Kullback–Leibler–based knowledge transfer. Extensive experiments on the TT-150k-genre dataset demonstrate the effectiveness of the proposed method. The code is publicly available on https://github.com/jing-1/DecCM [ABSTRACT FROM AUTHOR]

This article discusses the importance of responsible recommender systems, which are information filtering systems that suggest personalized recommendations to users. While existing recommender systems focus on accuracy and personalization, there is a growing need for these systems to also work responsibly to avoid negative effects on users and society. The article highlights various challenges and threats in technical, social, and ethical contexts that need to be addressed. It also presents recent advances and contributions in the field of responsible recommender systems, covering topics such as bias, debiasing, miscalibration, privacy and security, fairness, and explainability. [Extracted from the article]

The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proves that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user's needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model's accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism. [ABSTRACT FROM AUTHOR]

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research. [ABSTRACT FROM AUTHOR]

This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user's representation from a recommender to output a set of activated attributes describing user preferences. Besides encoding interpretability properties such as fidelity, conciseness and diversity, the proposed memory network-based interpreter enables the generalization of user representation by discovering relevant attributes that go beyond her adopted items' textual content. We design experiments involving both human- and functionally-grounded evaluations of interpretability. Results on four real-world datasets show that our proposed model not only discovers highly relevant attributes for interpreting user preferences, but also enjoys comparable or better recommendation accuracy than a series of baselines. [ABSTRACT FROM AUTHOR]

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond. [ABSTRACT FROM AUTHOR]

User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose the neighbors' rating data to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors' ratings, which unfortunately reduces the accuracy of UserKNN. In this work, we introduce ReuseKNN, a novel differentially private KNN-based recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy and (ii) most users do not need to be protected with differential privacy since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations. Firstly, ReuseKNN requires significantly smaller neighborhoods and, thus, fewer neighbors need to be protected with differential privacy compared with traditional UserKNN. Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN leads to significantly less privacy risk for users than in the case of UserKNN. [ABSTRACT FROM AUTHOR]

Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this article, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two "embedding maps". In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks (CNN) with kernels of different sizes simultaneously. A fully connected multi-layer perceptron (MLP) is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and rationality of our proposed method. [ABSTRACT FROM AUTHOR]

The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality negative information. Capturing negative signals in positive and unlabeled data is challenging for recommendation systems. Most studies have used specific data and proposed negative sampling methods suitable to the data characteristics. Existing negative sampling strategies cannot automatically select suitable approaches for different data. However, this one-size-fits-all strategy often makes potential positive samples considered as negative, or truly negative samples considered as potential positive samples and recommend to users. In this way, it will not only turn down the recommendation result, but even also have an adverse effect. Accordingly, we propose a novel negative sampling model, Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation (RHNSR), which can combine multiple sampling strategies and dynamically adjust the proportions used by different sampling strategies. In addition, ensemble learning, which integrates various model sampling strategies for obtaining an improved solution, was applied to RHNSR. Extensive experiments were conducted on three real-world recommendation datasets, and the experimental results indicated that the proposed model significantly outperformed state-of-the-art baseline models and revealed significant improvements in precision and hit ratio (49.02% and 37.41%, respectively). [ABSTRACT FROM AUTHOR]

The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic approach to selecting and composing an LR policy for effective DNN training to meet desired target accuracy and reduce training time within the pre-defined training iterations. It makes three original contributions. First, we develop an LR tuning mechanism for auto-verification of a given LR policy with respect to the desired accuracy goal under the pre-defined training time constraint. Second, we develop an LR policy recommendation system (LRBench) to select and compose good LR policies from the same and/or different LR functions through dynamic tuning, and avoid bad choices, for a given learning task, DNN model, and dataset. Third, we extend LRBench by supporting different DNN optimizers and show the significant mutual impact of different LR policies and different optimizers. Evaluated using popular benchmark datasets and different DNN models (LeNet, CNN3, ResNet), we show that our approach can effectively deliver high DNN test accuracy, outperform the existing recommended default LR policies, and reduce the DNN training time by 1.6-6.7× to meet a targeted model accuracy. [ABSTRACT FROM AUTHOR]

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output of the recommendation model, which has two problems: (1) It is difficult to evaluate the produced explanations, because they are usually model-dependent, and (2) as a result, how the explanations impact the recommendation performance is less investigated. In this article, explaining recommendations is formulated as a ranking task and learned from data, similarly to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., Normalized Discounted Cumulative Gain). Furthermore, this article extends traditional item ranking to an item–explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user–item interaction data, since not every user–item pair can be associated with all explanations. To mitigate this issue, this article proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution's effectiveness on both explanation ranking and item recommendation. [ABSTRACT FROM AUTHOR]

Existing cross domain recommender systems typically assume homogeneous user preferences across multiple domains to capture similarities of user-item interactions and to provide cross domain recommendations accordingly. Meanwhile, the heterogeneity of user behaviors is usually not well studied and captured during the recommendation process, where users might have vastly different interests in different domains. In addition, previous models focus primarily on recommendation tasks between domain pairs, and cannot be naturally extended to serve for multiple domain recommendation applications. To address these challenges, we propose to utilize the idea of adversarial learning to intelligently incorporate global user preferences and domain-specific user preferences for providing satisfying cross domain recommendations. In particular, our proposed Adversarial Cross Domain Recommendation (ACDR) model first obtains the latent representations of global user preferences from their explicit feature information, and then transforms them into domain-specific user embeddings, where we take into account user behaviors and their heterogeneous preferences among different domains. By doing so, we address the differences among user representations in the domain-specific latent space while also preserving global user preferences, as we effectively segment the distributions of domain-specific user embeddings in the shared latent space. The convergence of our proposed model is theoretically guaranteed. The proposed ACDR model leads to significant and consistent improvements in cross domain recommendation performance over the state-of-the-art baseline models, which we demonstrate through extensive experiments on three real-world datasets. In addition, we show that the improvements are greater on those datasets that are smaller and more sparse, on those users that have fewer interaction records in the dataset, and when user interactions from more product domains are included in the cross domain recommendation model. [ABSTRACT FROM AUTHOR]

Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [38] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation. [ABSTRACT FROM AUTHOR]

Recommender Systems (RecSys) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for recommendations for groups has increased. A wide range of Group Recommender Systems (GRecSys) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys. Previous works evaluate their systems using different types of groups (sizes and cohesiveness), and most of such works focus on testing their systems using only one type of item, called Experience Goods (EG). As a consequence, it is hard to get consistent conclusions about the performance of GRecSys. We present the aggregation strategies and aggregation functions that GRecSys commonly use to aggregate group members' preferences. This study experimentally compares the performance (i.e., accuracy, ranking quality, and usefulness) using four metrics (Hit Ratio, Normalize Discounted Cumulative Gain, Diversity, and Coverage) of eight representative RecSys for group recommendations on ephemeral groups. Moreover, we use two different aggregation strategies, 10 different aggregation functions, and two different types of items on two types of datasets (EG and Search Goods (SG)) containing real-life datasets. The results show that the evaluation of GRecSys needs to use both EG and SG types of data, because the different characteristics of datasets lead to different performance. GRecSys using Singular Value Decomposition or Neural Collaborative Filtering methods work better than others. It is observed that the Average aggregation function is the one that produces better results. [ABSTRACT FROM AUTHOR]

Recommender systems are essential components of many information services, which aim to find relevant items that match user preferences. Several studies have shown that shilling attacks can significantly weaken the robustness of recommender systems by injecting fake user profiles. Traditional shilling attacks focus on creating hand-engineered fake user profiles, but these profiles can be detected effortlessly by advanced detection methods. Adversarial learning, which has emerged in recent years, can be leveraged to generate powerful and intelligent attack models. To this end, in this article we explore potential risks of recommender systems and shed light on a gray-box shilling attack model based on generative adversarial networks, named GSA-GANs. Specifically, we aim to generate fake user profiles that can achieve two goals: unnoticeable and offensive. Toward these goals, there are several challenges that we need to address: (1) learning complex user behaviors from user-item rating data, and (2) adversely influencing the recommendation results without knowing the underlying recommendation algorithms. To tackle these challenges, two essential GAN modules are respectively designed to make generated fake profiles more similar to real ones and harmful to recommendation results. Experimental results on three public datasets demonstrate that the proposed GSA-GANs framework outperforms baseline models in attack effectiveness, transferability, and camouflage. In the end, we also provide several possible defensive strategies against GSA-GANs. The exploration and analysis in our work will contribute to the defense research of recommender systems. [ABSTRACT FROM AUTHOR]

Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel framework Fedrated Social recommendation with Graph neural network (FeSoG). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge. [ABSTRACT FROM AUTHOR]

Recommender systems typically require the revelation of users' ratings to the recommender server, which will subsequently use these ratings to provide personalized services. However, such revelations make users vulnerable to a broader set of inference attacks, allowing the recommender server to learn users' private attributes, e.g., age and gender. Therefore, in this paper, we propose an efficient federated matrix factorization method that protects users against inference attacks. The key idea is that we obfuscate one user's rating to another such that the private attribute leakage is minimized under the given distortion budget, which bounds the recommending loss and overhead of system efficiency. During the obfuscation, we apply differential privacy to control the information leakage between the users. We also adopt homomorphic encryption to protect the intermediate results during training. Our framework is implemented and tested on real-world datasets. The result shows that our method can reduce up to 16.7% of inference attack accuracy compared to using no privacy protections. [ABSTRACT FROM AUTHOR]

Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this article, we present Axolotl (AutomatedcrossLocation-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer: (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the US, Japan, and Germany, using three as source regions and nine of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics. [ABSTRACT FROM AUTHOR]

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches. [ABSTRACT FROM AUTHOR]

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS)2-Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers). [ABSTRACT FROM AUTHOR]

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a, using all attributes of its h-hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a. We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this article, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications. [ABSTRACT FROM AUTHOR]

It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users' distributions. In this work, we demonstrate that a lack of flatness in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations. [ABSTRACT FROM AUTHOR]

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article, we describe the qualitative characteristics of the dataset and present the comparison of several baseline algorithms trained on the data. [ABSTRACT FROM AUTHOR]

Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider a user's preference of price range for individual clothing types or an overall shopping budget for a set of items. In this article, we propose a box recommendation framework—BOXREC—which at first collects user preferences across different item types (namely, top-wear, bottom-wear, and foot-wear) including price range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price ranges), creates all possible combinations of three preferred items (belonging to distinct item types), and verifies each combination using an outfit scoring framework—BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. We create an extensively annotated dataset of male fashion items across various types and categories (each having associated price) and a manually annotated positive and negative formal as well as casual outfit dataset. We consider a set of recently published pairwise compatibility prediction methods as competitors of BOXREC-OSF. Empirical results show superior performance of BOXREC-OSF over the baseline methods. We found encouraging results by performing both quantitative and qualitative analysis of the recommendations produced by BOXREC. Finally, based on user feedback corresponding to the recommendations given by BOXREC, we show that disliked or unpopular items can be a part of attractive outfits. [ABSTRACT FROM AUTHOR]

Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time. Previous unexpected recommendation methods only focus on the straightforward relations between current recommendations and user expectations by modeling unexpectedness in the feature space, thus resulting in the loss of accuracy measures to improve unexpectedness performance. In contrast to these prior models, we propose to model unexpectedness in the latent space of user and item embeddings, which allows us to capture hidden and complex relations between new recommendations and historic purchases. In addition, we develop a novel Latent Closure (LC) method to construct a hybrid utility function and provide unexpected recommendations based on the proposed model. Extensive experiments on three real-world datasets illustrate superiority of our proposed approach over the state-of-the-art unexpected recommendation models, which leads to significant increase in unexpectedness measure without sacrificing any accuracy metric under all experimental settings in this article. [ABSTRACT FROM AUTHOR]

Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known problem with review fraud detection systems is their underlying assumption that the majority of reviews are honest-this assumption leads to a vulnerability where an attacker can try to generate many fake reviews of a product. In this article, we consider the case where a company wishes to fraudulently promote its product through fake reviews and propose the Sockpuppet-based Targeted Attack on Reviewing Systems (STARS for short). STARS enables an attacker to enter fake reviews for a product from multiple, apparently independent, sockpuppet accounts. We show that the STARS attack enables companies to successfully promote their product against seven recent, well-known review fraud detectors on four datasets (Amazon, Epinions, and the BitcoinAlpha and OTC exchanges) by significant margins. To protect against the STARS attack, we propose a new fraud detection algorithm called RTV. RTV introduces a new class of users (called trusted users) and also considers reviews left by verified users which were not considered in existing review fraud detectors. We show that RTV significantly mitigates the impact of the STARS attack across the four datasets listed above. [ABSTRACT FROM AUTHOR]

As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products. [ABSTRACT FROM AUTHOR]

Recommender Systems are tools designed to help users find relevant information from the myriad of content available online. They work by actively suggesting items that are relevant to users according to their historical preferences or observed actions. Among recommender systems, top-N recommenders work by suggesting a ranking of N items that can be of interest to a user. Although a significant number of top-N recommenders have been proposed in the literature, they often disagree in their returned rankings, offering an opportunity for improving the final recommendation ranking by aggregating the outputs of different algorithms. Rank aggregation was successfully used in a significant number of areas, but only a few rank aggregation methods have been proposed in the recommender systems literature. Furthermore, there is a lack of studies regarding rankings' characteristics and their possible impacts on the improvements achieved through rank aggregation. This work presents an extensive two-phase experimental analysis of rank aggregation in recommender systems. In the first phase, we investigate the characteristics of rankings recommended by 15 different top-N recommender algorithms regarding agreement and diversity. In the second phase, we look at the results of 19 rank aggregation methods and identify different scenarios where they perform best or worst according to the input rankings' characteristics. Our results show that supervised rank aggregation methods provide improvements in the results of the recommended rankings in six out of seven datasets. These methods provide robustness even in the presence of a big set of weak recommendation rankings. However, in cases where there was a set of non-diverse high-quality input rankings, supervised and unsupervised algorithms produced similar results. In these cases, we can avoid the cost of the former in favor of the latter. [ABSTRACT FROM AUTHOR]

Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this article, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules, a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality: it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tools. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users. [ABSTRACT FROM AUTHOR]

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought…". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as "Campus radio plays this song at noon every day, and I think it sounds wonderful," which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations. [ABSTRACT FROM AUTHOR]

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other's physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction, and so forth. In this article, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+, an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between January 2015 and February 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation. [ABSTRACT FROM AUTHOR]

Recommender systems are common in e-commerce platforms in recent years. Recommender systems are able to help users find preferential items among a large amount of products so that users' time is saved and sellers' profits are increased. Cross-domain recommender systems aim to recommend items based on users' different tastes across domains. While recommender systems usually suffer from the user cold-start problem that leads to unsatisfying recommendation performance, cross-domain recommendation can remedy such a problem. This article proposes a novel cross-domain recommendation model based on regression analysis, partial least squares regression (PLSR). The proposed recommendation models, PLSR-CrossRec and PLSR-Latent, are able to purely use source-domain ratings to predict the ratings for cold-start users who never rated items in the target domains. Experiments conducted on the Epinions dataset with ten various domains' rating records demonstrate that PLSR-Latent can outperform several matrix factorization-based competing methods under a variety of cross-domain settings. The time efficiency of PLSR-Latent is also satisfactory. [ABSTRACT FROM AUTHOR]

In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Numerical experiments have been performed over the instances generated using the realistic public transit dataset in Singapore. The results show that we can find significantly better solutions than the deterministic formulation, and in most cases we can find solutions that are very close to the theoretical performance limit. To demonstrate the practicality of our approach, we deployed our recommendation engine to a campus-scale field trial, and we demonstrate that workers receiving our recommendations incur fewer detours and complete more tasks, and are more efficient against workers relying on their own planning (25% more for top workers who receive recommendations). This is achieved despite having highly uncertain worker trajectories. We also demonstrate how to further improve the robustness of the system by using a simple multi-coverage mechanism. [ABSTRACT FROM AUTHOR]

Users in real-world social networks are organized into communities that differ from each other in terms of influence, authority, interest, size, etc. This article addresses the problems of detecting communities of authority and of estimating the influence of such communities in dynamic social networks. These are new issues that have not yet been addressed in the literature, and they are important in applications such as marketing and recommender systems. To facilitate the identification of communities of authority, our approach first detects communities sharing common interests, which we call “meta-communities,” by incorporating topic modeling based on users’ community memberships. Then, communities of authority are extracted with respect to each meta-community, using a new measure based on the betweenness centrality. To assess the influence between communities over time, we propose a new model based on the Granger causality method. Through extensive experiments on a variety of social network datasets, we empirically demonstrate the suitability of our approach for community-of-authority detection and assessment of the influence between communities over time. [ABSTRACT FROM AUTHOR]