Search

Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS. [ABSTRACT FROM AUTHOR]

AbstractPeople use diverse read-it-later tools to save webpages for future uses; however, difficulties in scrap retrieval remain. Our study aims to understand users’ mental models of read-it-later tools and suggests a new tool, Read-It-Now, for web scraping. We conducted interviews to identify web scraping habits and developed four main design recommendations for read-it-later tools: tool hierarchy, contextual aids, proactive triggers, and multiple navigation strategies. With a focus on the proactive trigger that facilitates scrap retrieval, Read-It-Now is designed to recommend relevant web scraps based on a comparison of the title of the current browser tab. We tested the design prototype against traditional bookmarks and found that users preferred Read-It-Now because of its simplicity and effectiveness; however, explanations for scrap recommendations could be improved. [ABSTRACT FROM AUTHOR]

The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations. Our model incorporates a double-tower network (DT-Net) that learns user and item representations through a meta-encoder and a mutual attention encoder, effectively mitigating the impact of noisy data on auxiliary information. By framing the cold-start problem as few-shot meta-learning, we employ a model-agnostic meta-optimization strategy to train the model across a variety of tasks during the meta-learning phase. Extensive experiments conducted on three real-world datasets—ShortVideos, MovieLens, and Book-Crossing—demonstrate the superiority of our model in cold-start recommendation scenarios. Compared to MetaCs-DNN, the second-best approach, CFSM, achieves improvements of 1.55%, 1.34%, and 2.42% under the AUC metric on the three datasets, respectively. [ABSTRACT FROM AUTHOR]

Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users' demands. Foundational work in reranking has shown the potential of improving recommendation results by uncovering mutual influence among items. However, rather than considering the context of initial lists as most existing methods do, an ideal reranking algorithm should consider the counterfactual context—the position and the alignment of the items in the reranked lists. In this work, we propose a novel pairwise reranking framework, Utility-oriented Reranking with Counterfactual Context (URCC), which maximizes the overall utility after reranking efficiently. Specifically, we first design a utility-oriented evaluator, which applies Bi-LSTM and graph attention mechanism to estimate the listwise utility via the counterfactual context modeling. Then, under the guidance of the evaluator, we propose a pairwise reranker model to find the most suitable position for each item by swapping misplaced item pairs. Extensive experiments on two benchmark datasets and a proprietary real-world dataset demonstrate that URCC significantly outperforms the state-of-the-art models in terms of both relevance-based metrics and utility-based metrics. [ABSTRACT FROM AUTHOR]

Recommender systems play a crucial role in providing product information for online transaction. The suitable implementation of these systems influenced marketing achievement significantly. Collaborative filtering, which utilizes customers' past activities such as ratings, is one type of recommender system model. However, the data collected from customer ratings is often sparse, comprising less than 4% and other datasets below 1% of potential input. Aim to handle this issue, the recommender system model employs matrix factorization to make rating predictions. Nevertheless, using only matrix factorization algorithm may result in inaccurate recommendations. Some algorithms incorporate item information in the term item review to enhance rating prediction robustness by utilizing techniques like bag of word (BoW) and Latent Dirichlet Allocation (LDA) for interpreting these documents. However, traditional methods like LDA and BoW exhibit limitations in their contextual understanding of the document content. In response to this challenge, current research integrates matrix factorization with novel approaches that leverage Convolutional Neural Network (CNN) and word embedding model based on Global Word Vector Representation (GloVe) for deeper document understanding. Our proposed model used novel model based on hybrid approach used Probabilistic Matrix Factorization (PMF), BERT (Bidirectional Encoder Representation for Transformer) and Attention module. Experimental results indicate that this integrated model significantly outperforms traditional latent factors by an average margin exceeding 16%, achieving around 2% over previous work based on CNN, PMF and Word Embedding using Root Mean Squared Error (RMSE) evaluation metrics. The performance of our model comparison over Singular Value Decomposition (SVD) and temporal effect, PMF and CNN, PMF and LSTM (Long Short-Term Memory) as best previous work. [ABSTRACT FROM AUTHOR]

Session-based recommendations which aim to predict subsequent user–item interactions based on historical user behaviour during anonymous sessions can be challenging to carry out. Two main challenges need to be addressed and improved: (1) how does one analyze these sessions to accurately and completely capture users' preferences, and (2) how does one identify and eliminate any interference caused by noisy behavior? Existing methods have not adequately addressed these issues since they either neglect the valuable insights that can be gained from analyzing consecutive groups of items or fail to take these noisy data in sessions seriously and handle them properly, which can jointly impede recommendation systems from capturing users' real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths into varying multi-order semantic units to mine the user's primary intentions from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The results of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendations. [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]

With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users' sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items' content attributes and users' sensitive information. In this article, we propose DALFRec, a fairness-aware recommendation algorithm based on user-side and item-side adversarial learning to mitigate the effects of sensitive information on both sides of the recommendation process. First, we conduct a statistical analysis to demonstrate the latent relationship between items' information and users' sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users' sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items' content attributes and users' sensitive attributes to better assess the algorithm's ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the digital era, organizations belonging to the same or different market segments come together in digital platforms that allow them to exchange. These organizations are unified within a Digital Business Ecosystem. However, the rapid growth of the number of these organizations accentuates the complexity of finding economic partners, customers, suppliers, or other organizations that can share economic interests. In our research, we propose a recommendation system that is implemented on such a digital platform, and which is based on matchmaking and hybrid supervised machine learning algorithms. In this paper, we provide a detailed analysis of the functioning of this system, the challenge encountered when processing the data which made it possible to highlight the similarities between the organizations that can be associated. Thus, we seek to improve the understanding and analysis of the data for the identification of partners in an optimal way. [ABSTRACT FROM AUTHOR]

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user's subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user's past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model's success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%. [ABSTRACT FROM AUTHOR]

The study presents a Cluster-Based Cab Recommender System (CBCRS) designed to optimize cab services by suggesting the nearest locations with a higher likelihood of finding passengers. To achieve this, the system employs advanced clustering techniques to cluster historical cab pickup locations, identifying areas with higher passenger possibilities at specific times and days. The research aims to develop an algorithmic framework for CBCRS based on a hybrid clustering technique. The objectives of the study are twofold: first, to identify current clustering techniques used in clustering cab pickup geo-points, and second, to propose a framework for CBCRS based on the most efficient clustering technique. This framework will accept the current location of the cab driver and recommend the next nearest passenger pickup location. Additionally, the study compares and contrasts the proposed system with other clustering techniques using three standard datasets, evaluating them based on intrinsic measures such as the Calinski-Harabasz Index and Silhouette-Score. The paper concludes by evaluating and contrasting the proposed CBCRS framework with different clustering techniques, analyzing the results using statistical parameters. The findings reveal that the proposed CBCRS system generates better recommendations for the cab drivers using CBCRS hybrid clustering technique as compared to K-Means, BIRCH, DBSCAN clustering algorithms. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness. [ABSTRACT FROM AUTHOR]

Recommender systems are used in various fields such as movies, music, and social networks. Recommender systems aim to provide attractive offers to users according to their performance in the system. The most popular recommender systems are content-based models and collaborative filtering methods. One of the most important challenges and problems in recommender systems is the challenge of users’ cold start. So far, various methods such as machine learning algorithms, optimization approaches, and statistical methods, have been proposed by other researchers in improving internet marketing strategy and overcoming the cold-start problem, which despite having numerous applications, still could not solve the start problem. This article will investigate the problem of cold start users’ by presenting a recommendation model based on a deep neural network and considering the problem of improving the internet (network) marketing strategy. In this article, the relevant simulation is done on the popular Movielens dataset, which is from 2015, and the evaluations of the methods presented on this dataset are compared [ABSTRACT FROM AUTHOR]

Session-based recommendation aims to generate recommendations for the next item of users' interest based on a given session. In this manuscript, we develop intention enhanced mixed attentive model (IMAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' intentions. Unlike existing methods which primarily leverage complicated gated recurrent units to model the temporal patterns, IMAM models the temporal patterns using a light-weight while effective position-sensitive attention mechanism. In IMAM, we also leverage the estimate of users' prospective preferences to signify important items, and generate better recommendations. Our experimental results demonstrate that IMAM models significantly outperform the state-of-the-art methods in six benchmark datasets, with an improvement as much as 19.2%. In addition, our run-time performance comparison demonstrates that during testing, IMAM models are much more efficient than the best baseline method, with a significant average speedup of 47.7 folds. [ABSTRACT FROM AUTHOR]

Recommender systems are vulnerable to attacks because of their open nature. Counterfeit users give biased ratings to the items due to various objectives that may lead to the loss of user trust. The attackers use certain attack models with specific features. The existing attack detection techniques are typically attack-specific and work only when the attack features are known. They are unable to identify an unknown attack with unfamiliar features. To diminish this problem, in this paper, we propose a generalized solution that filters any attack irrespective of its design and features. We trained the classifiers with the ratings of the known authentic users using one-class SVM and PU learning models for detecting attacks, considering their ability to detect anomalies in the dataset caused by unknown attacks. The openly available MovieLens dataset has been used to assess our designed attack detection method. The experimental results show that all unknown attacks are successfully detected with 100% accuracy. The same detection accuracy is achieved for attacks with known features. [ABSTRACT FROM AUTHOR]

In light of the remarkable capacity of graph convolutional network (GCN) in representation learning, researchers have incorporated it into collaborative filtering recommendation systems to capture high-order collaborative signals. However, existing GCN-based collaborative filtering models still exhibit three deficiencies: the failure to consider differences between users' activity and preferences for items' popularity, the low-order feature information of users and items has been inadequately employed, and neglecting the correlated relationships among isomorphic nodes. To address these shortcomings, this paper proposes a degree-aware embedding-based multi-correlated graph convolutional collaborative filtering (Da-MCGCF). Firstly, Da-MCGCF combines users' activity and preferences for items' popularity to perform neighborhood aggregation in the user-item bipartite graph, thereby generating more precise representations of users and items. Secondly, Da-MCGCF employs a low-order feature fusion strategy to integrate low-order features into the process of mining high-order features, which enhances feature representation capabilities, and enables the exploration of deeper relationships. Furthermore, we construct two isomorphic graphs by employing an adaptive approach to explore correlated relationships at the isomorphic level between users and items. Subsequently, we aggregate the features of isomorphic users and items separately to complement their representations. Finally, we conducted extensive experiments on four public datasets, thereby validating the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

User–item bipartite graphs and knowledge graphs are frequently employed in recommender systems due to their ability to provide rich information for user and item modeling. However, existing recommender systems predominantly focus on modeling either the user or item individually, with few studies simultaneously considering both aspects. In this paper, we propose a novel double-ended recommendation model, DEKGCI, which aims to fully leverage the advantages of these two information sources. Specifically, DEKGCI harnesses the high-order connectivity information in the user–item bipartite graph to extract user representations, while utilizing the semantic information in the knowledge graph to enrich item representations. By doing so, DEKGCI concurrently learns both user and item representations, effectively capturing the intricate interaction information between users and items. The DEKGCI model was evaluated on three real-world datasets. Computational results demonstrate the high effectiveness of the proposed DEKGCI model compared to seven state-of-the-art reference methods from the literature. In particular, compared to the best-performing KFGAN model, DEKGCI achieved AUC gains of 0.335% in movie recommendations, and F1 gains of 0.023%, 2.203%, and 0.530% in movie, book, and music recommendations, respectively. The code and data are available at https://github.com/miaomiao924/DEKGCI. [ABSTRACT FROM AUTHOR]

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users' demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

In recent years, graph neural networks (GNNs) have been widely applied in recommender systems. However, existing recommendation algorithms based on GNNs still face challenges in node aggregation and feature extraction processes because they often lack the ability to capture the interactions between users and items, as well as users' multiple intentions. This hinders accurate understanding of users' needs. To address the aforementioned issues, we propose a recommendation model called multi-intent-aware interactive graph convolutional network (Multi-IAIGCN). This model is capable of integrating multiple user intents and adopts an interactive convolution approach to better capture the information on the interaction between users and items. First, before the interaction between users and items begins, user intents are divided and mapped into a graph. Next, interactive convolutions are applied to the user and item trees. Finally, by aggregating different features of user intents, predictions of user preferences are made. Extensive experiments on three publicly available datasets demonstrate that Multi-IAIGCN outperforms existing state-of-the-art methods or can achieve results comparable to those of existing state-of-the-art methods in terms of recall and NDCG, thus verifying the effectiveness of Multi-IAIGCN. [ABSTRACT FROM AUTHOR]

Appropriately calibrated human trust is essential for successful Human-Agent collaboration. Probabilistic frameworks using a partially observable Markov decision process (POMDP) have been previously employed to model the trust dynamics of human behavior, optimising the outcomes of a task completed with a collaborative recommender system. A POMDP model utilising signal detection theory to account for latent user trust is presented, with the model working to calibrate user trust via the implementation of three distinct agent features: disclaimer message, request for additional information, and no additional feature. A simulation experiment is run to investigate the efficacy of the proposed POMDP model compared against a random feature model and a control model. Evidence demonstrates that the proposed POMDP model can appropriately adapt agent features in-task based on human trust belief estimates in order to achieve trust calibration. Specifically, task accuracy is highest with the POMDP model, followed by the control and then the random model. This emphasises the importance of trust calibration, as agents that lack considered design to implement features in an appropriate way can be more detrimental to task outcome compared to an agent with no additional features. [ABSTRACT FROM AUTHOR]

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy. [ABSTRACT FROM AUTHOR]

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios. [ABSTRACT FROM AUTHOR]

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners' decision support. We have also developed a dedicated application for this project to visualize the results obtained. [ABSTRACT FROM AUTHOR]

The personalized recommender systems provide favorite services based on user preferences and interests. Due to the user's interests changing over time; hence the recommender system must be tracking these changes automatically; to overcome the research gap and col start problem in the current study, we suggest a framework to create an adaptive user profiling for a personalized recommender system using learning automata. We clustered items based on their features. In this technique, the learning automaton adjusts the amount of user interest in each cluster based on user feedback; then recommends the best items to the user based on demographic information of user and user's preferences. Several experiments are conducted on three movie datasets to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing approaches in terms of precision, recall, MAE, and RMSE. [ABSTRACT FROM AUTHOR]

One of the most effective and extensively used recommendation technique is collaborative filtering. Based on related users or items, collaborative filtering creates recommendations for the users. Similarity measures play a crucial role in Collaborative Filtering Recommender System. One useful similarity measure for a cold-start situation is the Proximity-Impact-Popularity (PIP) measure. The PIP measure is, nevertheless, subject to several limitations as it penalises users' multiple times throughout similarity computation in addition to ignoring their global rating behaviour. When a user rates the items, their overall rating behavior—whether they are lenient or strict—is referred to as global rating behaviour. In this study, we introduce an improved similarity metric to calculate similarity more accurately and generate high-quality recommendations. Our method takes into account both the user's rating behaviour and the percentage of co-rated items among users. Additionally, we have considered the computation complexity of the suggested work. In addition, to exhibit the performance of the proposed measure, empirical analysis has been done on real datasets. The results of the experiments performed on the dataset show that the suggested work takes precedence over the current similarity metrics. In comparison to state-of-the-art measurements, the suggested work exhibits improvements in MAE of 1.73%, RMSE of 4.01%, and F measure of 1.47% on an average. [ABSTRACT FROM AUTHOR]

Recommender systems are one of the most critical applications of AI, data science, and advanced analytics techniques because it has become integrated into our daily lives. Additionally, it serves as a powerful tool for making informed, effective, and efficient decisions and choices across a wide range of items. However, traditional techniques such as content-based and collaborative filtering often fail to consider the dynamic and short-term preferences of users when generating recommendations. To address this limitation, this research focuses on a session-based recommendation task using an XLNet transformer with various training strategies based on language modeling. Moreover, a dataset containing 102 million reviews of Amazon products was pre-processed to create two new datasets, one for a single domain and another for multi-domain data. A comparison between a GRU and the training strategies of XLNet reveals that the best training strategy achieves a 136.23% improvement in NDCG@20 and a 95.69% increase in Recall@20 for multi-domain data. In a single domain, it achieves a 168.81% improvement in NDCG@20 and a 25% increase in Recall@10. [ABSTRACT FROM AUTHOR]

Recommender systems (RSs) consist of predicting missing ratings based on the observed ones. This problem corresponds to matrix completion where users are its rows and items are its columns and it contains the observed ratings. An efficient RS is the one promoting the personal relevancy of its users which is not the case in the matrix completion process. It takes into account all the rates for prediction without including the users' and items' characteristics. Patterns are the key enablers of such solutions. In this work, we present a three-mode tensor representation with two aspects namely user–item interactions (observed ratings) and item–item relationship (detected similarities). To capture the similarities, the patterns are grouped in an equivalent manner using a bi-quantum clustering process (Quantum K-means). This step is adopted to consider only the relevant observed ratings in the prediction process and express item-to-item relationship using fidelity distance. Then, for each missing rating r that a user u might give to an item i in the future, a sub-tensor is created according to the detected patterns. This sub-tensor then is completed by minimizing its rank. This problem is NP-hard, hence a surrogate is used which is the nuclear norm. The effectiveness of the proposed approach is measured according to information retrieval evaluation criteria: precision, recall and F 1 -measure. The proposed approach improved the precision of the state-of-the-art methods by 20 %. [ABSTRACT FROM AUTHOR]

The personalized product preference and decision-making recommendation systems are highly demanded to handle big data and to increase service quality of the e-commerce platforms in the competitive industries. Previous recommender systems were hard coded and only extracted items from the same category. With this tactic, customers are limited to viewing only one category of products; items with several categories cannot be viewed. A concern for the e-commerce sector, particularly in the healthcare and pharmaceutical industries, is the growth of consumer preferences, the issue of cold starts, and the huge number of stocks holding units for new items. Therefore, this study aims to develop a product recommendation system for an e-commerce platform which deals with health supplements. For this reason, collaborative and content-based filtering are combined to propose a hybrid recommender system. In the proposed hybrid model, user's actions are converted into implicit rating weightage first. Then, to tackle the problem of increasing customer preferences, collaborative filtering is used to generate user's rating for warm-start items. Moreover, content-based filtering is used to solve cold start problem by recommending products to the users based on the similarity of the products regardless of user profile. Term frequency- inverse document frequency (TF-IDF) algorithm is adopted to weight the feature from the dataset first, then it creates step-by-step cosine similarity table. Finally, the proposed hybrid model is evaluated based on error metrices, ranking metrices, and business metrics and then compared based on the standard benchmarking algorithms. The best algorithm is selected to be used for the system development. Finally, the proposed hybrid model is developed and integrated into the real online e-commerce platform for healthcare company to handle the large number of stocks keeping units, cold start issues, and increasing customer preferences. This study can assist the healthcare companies to recommend relevant products to their customers and to help them stay competitive in healthcare e-commerce industry. [ABSTRACT FROM AUTHOR]

In this technical survey, the latest advancements in the field of recommender systems are comprehensively summarized. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. It starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, it provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field. [ABSTRACT FROM AUTHOR]

In recommender systems, learning representations of users and items is a crucial task for predicting user preferences. However, observational data suffer from inherent bias problems. Various confounding factors are present and lead to data biases, which result in incomplete and skewed representations and make it difficult to accurately determine a user's true preference. Recent studies have utilized unbiased data to alleviate the bias problem, but these methods do not learn about the representations of user interests, which are not affected by confounding factors. To address this gap, we propose a general disentangled framework, named DCRL, to learn causal representations for obtaining unbiased recommendations. We first analyze the interaction process in a recommender system based on causal graphs and propose that disentanglement can be achieved through intervening embeddings. DCRL leverages unbiased data as supervision signals to guide the disentanglement process, enabling causal representations to learn unbiased features and eliminate the effects of confounding factors. This approach is a model-agnostic solution because disentanglement is an additional task that can be implemented on basic recommendation models. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of DCRL in comparison with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Recommender systems are deployed heavily by many online platforms for better user engagement and providing recommendations. Despite being so popular, several works have shown the existence of popularity bias due to the non-random nature of missing data. Popularity bias leads to the recommendation of only a few popular items causing starvation of many non-popular items. This paper considers an easy-to-understand metric to evaluate the popularity bias as the difference between mean squared error on popular and non-popular items. Then, we propose EqBal-RS, a novel re-weighting technique that updates the weights of popular and non-popular items. Re-weighting ensures that both item sets are equally balanced during training using a trade-off function between overall loss and popularity bias. Our experiments on real-world datasets show that EqBal-RS outperforms the existing state-of-art algorithms in terms of accuracy, quality, and fairness. EqBal-RS works well on the proposed and existing popularity bias metrics and has significantly reduced runtime. The code is publicly available at https://github.com/eqbalrs/EqBalRS [ABSTRACT FROM AUTHOR]

Recommender systems are generally used in several domains, like e-commerce sites and social networks. E-learning systems use recommendation techniques to facilitate and improve online learning. Educational platforms offer users the necessary pedagogical tools to create an enriched learning environment, fostering collaboration and resource sharing. Recommender System faces many challenges. Among issues: (1) cold-start in which new users and/or items having not prior information available in the system; (2) data sparsity where rated items number is very small contrary to unrated items; and (3) scalability where more training data is required. This study presents a recommender system that uses learner criteria, such as learner's past behavior, demographics information, performance data, collaborative filtering, and ratings to suggest pedagogical resources. The proposed system adopts a hybrid approach, combining two primary methods: popularity-based and collaborative filtering-based. This hybrid approach enhances a collaborative filtering approach with popularity to provide a starting point for new users. The popularity-based is specifically used to address the issue of cold-start for new users by providing primary recommendations. Additionally, we have used two collaborative filtering approaches. The SVD-based enhances the recommendation list for the new user and tackles the sparsity problem. Simultaneously, enhanced matrix factorization with deep neural network (DNN) outperforms traditional matrix factorization in terms of recommendation diversity and accuracy. Our system improves the accuracy and effectively responds to user needs. Our approach early findings show promising results. It scores for top-10 items a total recall of (0.47), a global precision of (0.20), and an accuracy of (0.87). [ABSTRACT FROM AUTHOR]

This study introduces a novel movie recommender system utilizing a Semantic-Enhanced Variational Graph Autoencoder for Movie Recommendation (SeVGAER) architecture. The system harnesses additional information from movie plot summaries scraped from the internet, transformed into semantic vectors via a large language model. These vectors serve as supplementary features for movie nodes in the SeVGAER-based recommender. The system incorporates an encoder-decoder structure, operating on a user-movie bipartite graph, and employs GraphSAGE convolutional layers with modified aggregators as the encoder to extract latent representations of the nodes, and a Multi-Layer Perceptron (MLP) as the decoder to predict ratings with additional graph-based features. To address overfitting and improve model generalization, a novel training strategy is introduced. We employ a random data splitting approach, dividing the dataset into two halves for each training instance. The model then generates outputs on each half of the data, and a new loss function is introduced to ensure consistency between these two outputs, a strategy that can be seen as a form of contrastive learning. The model’s performance is optimized using a combination of this new contrastive loss, graph reconstruction loss, and KL divergence loss. Experiments conducted on the Movielens100k dataset demonstrate the effectiveness of this approach in enhancing movie recommendation performance. [ABSTRACT FROM AUTHOR]

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset. [ABSTRACT FROM AUTHOR]

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning Recommender Systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. Users posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. Innovative has been made in this work to propose a new e-learning recommendation system based on individualization and FSA. A new framework is proposed based on collaborative filtering models (CFMs) integrating with fine-grained sentiment analysis (FSA) for hybrid recommendation (CFISAR) for effective recommendations. CFMs attempt to capture the learner's latent factors based on their selections of interest to build the learner profile. FSA models are introduced to deliver e-content with the highest ranked ratings related to the learner's area and interests based on the extracted learner model. Moreover, a new approach is proposed to update the system continuously and not keep it bound to certain items by adding new books, where the initial rating of these new books is predicted based on FSA models. CFISAR is explored utilizing six CFMs to generate the prediction matrix and derive the learner model, resulting in a low MSE of 0.699 for Asymmetric SVD. The system used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context, and leveraging the goodness of deep learning, which predicted an accuracy of 0.9264% for the Peephole algorithm, that performed better than other models. [ABSTRACT FROM AUTHOR]

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user's attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item. [ABSTRACT FROM AUTHOR]

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC "smart" applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the "fog-based recommender systems" opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper. [ABSTRACT FROM AUTHOR]

In the current phase of technological development, Netflix has become a popular platform for entertainment. Often people feel overwhelmed when choosing a movie because of the variety of genres. To overcome this problem, a recommendation system is needed that can help people find the best movie according to their preferences. In addition, Twitter was used to collect tweets related to movies, which were then processed into rating values. The crawled dataset consisted of 855 movies and 44 user reviews (including data from IMDB, rotten tomatoes, and metacritic websites), for a total of 23,130 records. This research proposed to use the switching hybrid filtering (SHF) method combined with recurrent neural network (RNN) as classification. In SHF, the emphasis on rating prediction was initiated by the content based filtering method with RoBERTa, followed by switching to itembased collaborative filtering. This situation arose because the dataset had a sparseness of 74.46%. SHF provided accurate rating prediction with an MAE of 0.0617 and an RMSE of 0.1178. Nadam optimization with optimal learning rate in RNN classification gave the best results with an accuracy of 86.11%. The research successfully developed a method that proved effective and provided positive results, contributing to the development of a recommendation system designed to assist users in choosing movies based on their preferences. [ABSTRACT FROM AUTHOR]

Tourism is an important sector, serving as an avenue to show the natural resources of a country and inhabitants' hospitality. This sector creates several opportunities for building a potential market and enhancing economic activities where tourist spots and activities flourish. Despite the numerous benefits, tourism still requires significant improvement, particularly in the Philippines, where there are abundant beautiful places. Therefore, this study aimed to develop a recommender system based on users and content collaborative filtering to provide local and foreign tourists with viable information for experience improvement. The investigation focused on improving tourist satisfaction based on three aspects such as preferences, ratings, and reviews that add options for tourist spots, activities/itineraries, destinations, and others. The machine algorithm-based journey assistant (MAJA) was designed as an interface and agent in providing help to tourists. The mean average precision (MAP) and recall were used as evaluation metrics to better understand the ability of MAJA to offer personalized experiences to unique users. The results showed that integration of the system into tourism provided a smart platform for enhancing tourist experience and destination competitiveness. Consequently, successful implementation of the system is measured by two criteria, namely the degree of tourists' pleasure during trip and the capacity of MAJA to effectively transfer tourism to less popular and less "accessible" sector. [ABSTRACT FROM AUTHOR]

In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems' push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process. Furthermore, our proposed model demonstrates notable advantages in production scenarios. Specifically, it exhibits enhanced efficiency, manifesting in accelerated processing times and improved resource utilization, thereby contributing to a more sustainable and cost-effective training of machine learning solutions in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, we aim to improve existing health recommender systems by defining relevant contextual and motivational variables to recommend physical activities and collect appreciation feedback. Current health recommenders do not sufficiently include users' context and motivational theory when personalizing health suggestions. To bridge these gaps, we conducted a 21-day longitudinal user study with 36 participants using our Android app with collected sensor data and Ecological Momentary Assessments to collect daily activities, mood, and motivation. This study resulted in a dataset of 724 activities. Two approaches to determine feature relevance were followed: variable importances analysis on 40 input variables, and statistical analysis of mean differences in outcome variables across contexts. Our findings suggest recommending activity duration, intensity, location, and type by incorporating: company, situation (e.g., free time or work), happiness, calmness, energy level, physical complaints, and motivation. As such, we propose opportunities for future health recommenders to integrate these data with contextual pre-filtering techniques, extended with our suggestions for automatically collected weather, location types, step count, and time. We also propose to use mood and motivation as appreciation feedback to focus on user well-being and boost motivation. [ABSTRACT FROM AUTHOR]

The knowledge graph in the recommendation system plays a vital role in the recommendation effect of the system, and the knowledge representation in the graph becomes a key factor affecting the recommendation system, which has become one of the current research hotspots. This paper proposed a node2vec-based knowledge representation node2 vec-side based on the traditional node2vec model by adding relational representation and diversifing wandering strategy to the structural characteristics of the knowledge graph in recommendation system, which combined with the knowledge graph network structure of recommendation system to explore the potential association relationship between nodes of large-scale recommendation entities, reduced the complexity of the representation and improved interpretability. After time complexity analysis, it could be seen that the proposed knowledge representation is lower than Trans series and RGCN in terms of complexity. Link prediction experiments were conducted on the traditional knowledge graph datasets FB15K, WN18, and recommendation domain datasets MovieLens-1M, Book-Crossing, Last. FM respectively. The experimental results show that on the MovieLens-1M dataset, hits @ 10 improves 5.5%12.1% and MRR improves 0. 090. 24, respectively. On the Book-Crossing dataset, hits @ 10 improves 3.5%-20.6%, and MRR improves 0.04-0.24 on average, respectively. And on the Last. FM dataset, hits@1 improves 0.3%-8.5% and MRR improves 0.04-0. 16 on average. It is better than the existing algorithms and verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post), with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to assess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their potential for both academic and practical applications in the field. [ABSTRACT FROM AUTHOR]

The proliferation of uncategorized information on the Internet has intensified the need for effective recommender systems. Recommender systems have evolved from content-based filtering to collaborative filtering and, most recently, to deep learning-based and hybrid models. However, they often face challenges such as high computational costs, reduced reliability, and the Cold Start problem. We introduce a persona-based user modeling approach for real-time movie recommendations. Our system employs Non-negative Matrix Factorization (NMF) and Deep Learning algorithms to manage complex and sparse data types and to mitigate the Cold Start issue. Experimental results, based on criteria involving 50 topics and 35 personas, indicate a significant performance gain. Specifically, with 500 users, the precision@K for NMF was 86.01%, and for the Deep Neural Network (DNN), it was 92.67%. Tested with 900 users, the precision@K for NMF increased to 97.04%, and for DNN, it was 95.55%. These results represent an approximate 10% and 5% improvement in performance, respectively. The system not only delivers fast and accurate recommendations but also reduces computational overhead by updating the model only when user personas change. The generated user personas can be adapted for other recommendation services or large-scale data mining. [ABSTRACT FROM AUTHOR]

Recommender systems help users obtain the content they need from massive amounts of information. Artwork recommender systems is a topic that has attracted attention. However, existing art recommender systems rarely consider user preferences and multimodal information at the same time, while utilizing all the information has the potential to help make better personalized recommendations. To better apply recommender systems to the artwork-recommendation scenario, we propose a new neural topic modeling (NTM)-based multimodal artwork recommender system (MultArtRec), that can take all the information into account at the same time and extract effective features representing user preferences from multimodal content. Also, to improve MultArtRec's performance on monomodal feature extraction, we add a novel topic loss term to the conventional NTM loss. The first two experiments in this study compare the performances of different models with different monomodal inputs. The results show that MultArtRec can improve the performance with image modality inputs by up to 174.8% compared to the second-best model and improve the performance with text modality inputs by up to 10.7% compared to the second-best model. The third experiment is conducted to compare the performance of MultArtRec with monomodal inputs and multimodal inputs. The results show that the performance of MultArtRec with multimodal inputs can be improved by up to 15.9% compared to monomodal inputs. The last experiment preliminarily tests the versatility of MultArtRec on a fashion recommendation scenario that considers clothing image content and user preferences. The results show that MultArtRec outperforms the other methods across all the metrics. [ABSTRACT FROM AUTHOR]

The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset. [ABSTRACT FROM AUTHOR]

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20–30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs. [ABSTRACT FROM AUTHOR]