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As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., “other”) to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A ${^2}$ 2 -GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph convolution network to characterize the complicated interactions among $<$ < users, items, attributes $>$ > . Furthermore, to learn the node representation, we adopt the message-passing strategy to aggregate the messages passed from the other directly linked types of nodes (e.g., a user or an attribute). Towards this end, we are capable of incorporating associate attributes to strengthen the user and item representation learning, and thus naturally solve the attribute missing problem. Given that for different users, the attributes of an item have different influence on their preference to this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information. Extensive experiments have been conducted on several publicly accessible datasets to justify our model, demonstrating that our model outperforms several state-of-the-art methods and demonstrate the effectiveness of our attention method. [ABSTRACT FROM AUTHOR]

Rating prediction is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness, existing methods focus on modeling the user-item interaction graph. The inherent drawback of such methods is that their performance is bound to the density of the interactions, which is however usually of high sparsity. More importantly, for a strict cold start user/item that neither appears in the training data nor has any interactions in the test stage, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. This leads to the capability of learning embeddings for the strict cold start users/items. Our AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. Moreover, we propose a new graph neural network variant, i.e., gated-GNN, to effectively aggregate various attributes of different modalities in a neighborhood. Empirical results on three real-world datasets demonstrate that our model yields significant improvements for strict cold start recommendations and outperforms or matches the state-of-the-art performance in the warm start scenario. [ABSTRACT FROM AUTHOR]

Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors. The problem is that the inner product violates the triangle inequality property of feature vectors. As a result, it cannot well capture fine-grained attribute interactions, resulting in sub-optimal performance. Recently, the euclidean distance is exploited in FMs to replace the inner product and has delivered better performance. However, previous FM methods including the ones equipped with the euclidean distance all focus on the attribute-level interaction modeling, ignoring the critical intrinsic feature correlations inside attributes. Thereby, they fail to model the complex and rich interactions exhibited in the real-world data. To tackle this problem, in this paper, we propose a FM framework equipped with generalized metric learning techniques to better capture these feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network (DNN) methods, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a large margin. Moreover, we collect a new large-scale dataset on second-hand trading to justify the effectiveness of our method over cold-start and data sparsity problems in recommender systems. [ABSTRACT FROM AUTHOR]

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field. [ABSTRACT FROM AUTHOR]

Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness. [ABSTRACT FROM AUTHOR]

Classical algorithms in recommender systems (RS) mainly emphasis on achieving high accuracy and thus recommend items precisely matching a user's past choices. However, the user may gradually lose interest and crave something more inspiring. In psychology, curiosity is a critical human nature and can be efficient bootstrap exploratory behaviors, thus this phenomenon can be explained as insufficient stimulation to induce curiosity regard to recommended items. Inspired from the above, this work proposes a Curiosity-drive Recommendation Framework (CdRF) which incorporates a highly innovative Stimulus-evoked Curiosity mechanism (SeCM) together with a basic accuracy-oriented algorithm via Borda count. In SeCM, we first estimate the stimulus intensity appearing on each item for each user and then model personalized curiosity among the calculated intensities using Wundt curve. For the target user, the output of CdRF is a ranked list of N items which are both relevant and highly curiousness. We conduct extensive experiments using four public datasets to evaluate the performance of each specification of SeCM as well as the whole framework CdRF. The results reveal that SeCM can flexibly generate diversified items and CdRF can increase diversity in terms of ILS, Newness and AD while compromising very little Precision. This kind of research also offers a way to understand both individual differences in curiosity and how curiosity contributes to item exploration at the level of RS. [ABSTRACT FROM AUTHOR]

Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks. [ABSTRACT FROM AUTHOR]

Neural attention, an emerging technique used to identify important inputs within neural networks, have become increasingly popular in the area of recommender systems. Not only allowing to better identify what defines users and items, attention-based recommender systems are further able to provide accompanying explanations. However, these representations usually capture only part of users’ preferences and items’ attributes, resulting in limited reasoning and accuracy. We therefore propose Dual Attention Recommender with Items and Attributes (DARIA), a novel approach able to combine two dependable neural attention mechanisms to better justify its suggestions. Utilizing the personalized history of users, DARIA identifies the most relevant past activities while considering the real-world features that contributed to the similarity. In addition, we adopt the novel approach of self-attention and introduce Self-Attention Recommender based on Attributes and History (SARAH). As a variation to DARIA, SARAH utilizes two self-attention components to describe users by their most characteristic past activities and items by their best depicting attributes. Various experiments establish the significant improvement of SARAH and DARIA over seven key baselines in diverse recommendation settings. By comparing our two proposed frameworks, we demonstrate the potential benefit of applying self-attention in different scenarios. [ABSTRACT FROM AUTHOR]

Multi-label classification has gained in importance in the last decade and it is today confronted to the current needs to process massive raw data from heterogeneous sources. Therefore, dimensionality reduction, which aims at reducing the number of features, labels, or both, knows a renewed interest to enhance the scaling properties of the classifiers and their predictive performances. In this paper we review more than fifty papers presenting dimensionality reduction approaches for multi-label classification and we propose an analysis in three steps : (i) a typology of the methods describing the main components of their strategies, the problem they tackle and the way they solve it (ii) a unified formalization of the problems to help to distinguish the similarities and differences between the approaches, and (iii) a meta-analysis of the published experimental results inspired by the consensus theory to identify the most efficient algorithms. [ABSTRACT FROM AUTHOR]

As online video service continues to grow in popularity, video content providers compete hard for more eyeball engagement. Some users visit multiple video sites to enjoy videos of their interest while some visit exclusively one site. However, due to the isolation of data, mining and exploiting user behaviors in multiple video websites remain unexplored so far. In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China. The empirical study shows that users exhibit both consistent cross-site interests as well as site-specific interests. To represent this dichotomous pattern of user preferences, we propose a generative model of Multi-site Probabilistic Factorization (MPF) to capture both the cross-site as well as site-specific preferences. Besides, we discuss the design principle of our model by analyzing the sources of the observed site-specific user preferences, namely, site peculiarity and data sparsity. Through conducting extensive recommendation validation, we show that our MPF model achieves the best results compared to several other state-of-the-art factorization models with significant improvements of F-measure by 12.96, 8.24 and 6.88 percent, respectively. Our findings provide insights on the value of integrating user data from multiple sites, which stimulates collaboration between video service providers. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Online interactive recommender systems strive to promptly suggest users appropriate items (e.g., movies and news articles) according to the current context including both user and item content information. Such contextual information is often unavailable in practice, where only the users' interaction data on items can be utilized by recommender systems. The lack of interaction records, especially for new users and items, inflames the performance of recommendation further. To address these issues, both collaborative filtering, one of the most popular recommendation techniques relying on the interaction data only, and bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted into an online interactive recommendation setting assuming independent items (i.e., arms). This assumption rarely holds in reality, since the real-world items tend to be correlated with each other. In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items. We explicitly formulate item dependencies as the clusters of arms in the bandit setting, where the arms within a single cluster share the similar latent topics. In light of topic modeling techniques, we come up with a novel generative model to generate the items from their underlying topics. Furthermore, an efficient particle-learning based online algorithm is developed for inferring both latent parameters and states of our model by taking advantage of the fully adaptive inference strategy of particle learning techniques. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in an interactive collaborative filtering setting. Empirical studies on two real-world applications, online recommendations on movies and news, demonstrate both the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering and recommender systems. In this paper, we propose an algorithm named, Relative Pairwise Relationship constrained Non-negative Matrix Factorisation (RPR-NMF), which places constraints over relative pairwise distances amongst features by imposing penalties in a triplet form. Two distance measures, squared Euclidean distance and Symmetric divergence, are used, and exponential and hinge loss penalties are adopted for the two measures, respectively. It is well known that the so-called "multiplicative update rules" result in a much faster convergence than gradient descend for matrix factorisation. However, applying such update rules to RPR-NMF and also proving its convergence is not straightforward. Thus, we use reasonable approximations to relax the complexity brought by the penalties, which are practically verified. Experiments on both synthetic datasets and real datasets demonstrate that our algorithms have advantages on gaining close approximation, satisfying a high proportion of expected constraints, and achieving superior performance compared with other algorithms. [ABSTRACT FROM AUTHOR]

We develop a novel framework, named as $l$ -injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-Nrecommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion ofpre-use preferencesof users toward a vast amount ofunrateditems. Using this notion, we identifyuninterestingitems that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at: https://goo.gl/KUrmip. [ABSTRACT FROM AUTHOR]

Research of social recommendation aims at exploiting social information to improve the quality of a recommender system. It can be further divided into two classes. Explicit social recommendation assumes the existence of not only the users’ ratings on items, but also the explicit social connections between users. Implicit social recommendation assumes the availability of only the ratings but not the social connections between users, and attempts to infer implicit social connections between users with the goal to boost recommendation accuracy. This paper proposes a unified framework that is applicable to both explicit and implicit social recommendation. We propose an optimization framework to learn the degree of social correlation and rating prediction jointly, so these two tasks can mutually boost the performance of each other. Furthermore, a well-known challenge for implicit social recommendation is that it takes quadratic time to learn the strength of pairwise connections. This paper further proposes several practical tricks to reduce the complexity of our model to be linear to the observed ratings. The experiments show that the proposed model, with only two parameters, can significantly outperform the state-of-the-art solutions for both explicit and implicit social recommender systems. [ABSTRACT FROM AUTHOR]

Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. For this, we develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with i) high dimensionality due to a large number of items and ii) iterative estimation algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a novel binary mechanism based on sampling. We additionally introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate recommendation accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements. [ABSTRACT FROM AUTHOR]

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures. [ABSTRACT FROM PUBLISHER]

Nowadays, with the boom of social media and e-commerce, more and more people prefer to share their consumption experiences and rate services on review sites. Much research has focused on personalized recommendation. However, quality of service also plays an important role in recommender systems, and it is the main concern of this paper. An overall rating that indicates the popular view usually represents the evaluation. There are some challenges when we do not have enough review information to extract public opinion. Take, for example, a movie for which one user rates a two star rating, and another rates a five star rating. In this case, it is difficult to conduct a quality evaluation fairly. However, it is possible to be improved with the help of big social users’ contextual information. In this paper, we propose a model to conduct service quality evaluation by improving overall rating of services using an empirical methodology. We use the concept of user rating's confidence, which denotes the trustworthiness of user ratings. First, entropy is utilized to calculate user ratings’ confidence. Second, we further explore spatial-temporal features and review sentimental features of user ratings to constrain their confidences. Last, we fuse them into a unified model to calculate an overall confidence, which is utilized to perform service quality evaluation. Extensive experiments implemented on Yelp and Douban Movie datasets demonstrate the effectiveness of our model. [ABSTRACT FROM PUBLISHER]

Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach. [ABSTRACT FROM PUBLISHER]

Although the matrix completion paradigm provides an appealing solution to the collaborative filtering problem in recommendation systems, some major issues, such as data sparsity and cold-start problems, still remain open. In particular, when the rating data for a subset of users or items is entirely missing, commonly known as the cold-start problem, the standard matrix completion methods are inapplicable due the non-uniform sampling of available ratings. In recent years, there has been considerable interest in dealing with cold-start users or items that are principally based on the idea of exploiting other sources of information to compensate for this lack of rating data. In this paper, we propose a novel and general algorithmic framework based on matrix completion that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, our proposed recommender algorithm, dubbed DecRec, decouples the following two aspects of the cold-start problem to effectively exploit the side information: (i) the completion of a rating sub-matrix, which is generated by excluding cold-start users/items from the original rating matrix; and (ii) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference prevents the error propagation of completion and transduction, and also significantly boosts the performance when appropriate side information is incorporated. The recovery error of the proposed algorithm is analyzed theoretically and, to the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees on performance. Additionally, we also address the problem where both cold-start user and item challenges are present simultaneously. We conduct thorough experiments on real datasets that complement our theoretical results. These experiments demonstrate the effectiveness of the proposed algorithm in handling the cold-start users/items problem and mitigating data sparsity issue. [ABSTRACT FROM PUBLISHER]

Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW significantly outperforms existing approaches. [ABSTRACT FROM PUBLISHER]

The problem of point of interest (POI) recommendation is to provide personalized recommendations of places, such as restaurants and movie theaters. The increasing prevalence of mobile devices and of location based social networks (LBSNs) poses significant new opportunities as well as challenges, which we address. The decision process for a user to choose a POI is complex and can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. This is further complicated by the connection LBSNs and mobile devices. While there are some studies on POI recommendations, they lack an integrated analysis of the joint effect of multiple factors. Meanwhile, although latent factor models have been proved effective and are thus widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of LBSNs. To this end, in this paper, we propose a general geographical probabilistic factor model ( \sfGeo -PFM) framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user’s check-in behavior. Also, user mobility behaviors can be effectively leveraged in the recommendation model. Moreover, based our \sfGeo -PFM framework, we further develop a Poisson \sfGeo -PFM which provides a more rigorous probabilistic generative process for the entire model and is effective in modeling the skewed user check-in count data as implicit feedback for better POI recommendations. Finally, extensive experimental results on three real-world LBSN datasets (which differ in terms of user mobility, POI geographical distribution, implicit response data skewness, and user-POI observation sparsity), show that the proposed recommendation methods outperform state-of-the-art latent factor models by a significant margin. [ABSTRACT FROM PUBLISHER]

This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches. [ABSTRACT FROM PUBLISHER]

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds "neighbors" of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions. [ABSTRACT FROM PUBLISHER]

Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be avoided, designers resort to simplifying assumptions that impair the system's performance and utility. We have developed a novel technique referred to as CoFiDS—Collaborative Filtering based on Dempster-Shafer belief-theoretic framework—that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the domain expert can either obtain a "hard” decision or can narrow the set of possible predictions to a smaller set. With its capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as medical decision support systems and defense-related applications. We describe the theoretical foundation of the system and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS' behavior and show that its performance compares favorably with other ACF systems. [ABSTRACT FROM AUTHOR]