Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.@en

Recommendation system has became an important component in many real applications, ranging from e-commerce, music app to video-sharing site and on-line book store. The key of a successful recommendation system lies in the accurate user/item profiling. With the advent of web 2.0, quite a lot of multimodal information has been accumulated, which provides us with the opportunity to profile users in a more comprehensive manner. However, directly integrating multimodal information into recommendation system is not a trivial task, because they may be either homogenous or heterogeneous, which requires more advanced method for both fusion and alignment. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in fusing multi-dimensional information for user modeling and recommender systems. We hope more advanced technologies can be proposed or inspired, and also we hope that the direction of integrating different types of information can catch much more attention in both academic and industry.

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We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.@en

User-based collaborative filtering, a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts.@en

The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.

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Collaborative filtering suffers from the problems of data spar-sity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TntstSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item-and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.

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User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance.

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Trust is one source of information that has been widely adopted to personalize online services for users, such as in product recommendations. However, trust information is usually very sparse or unavailable for most online systems. To narrow this gap, we propose a principled approach that predicts implicit trust from users' interactions, by extending a well-known trust antecedents framework. Specifically, we consider both local and global trustworthiness of target users, and form a personalized trust metric by further taking into account the active user's propensity to trust. Experimental results on two real-world datasets show that our approach works better than contemporary counterparts in terms of trust ranking performance when direct user interactions are limited.

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Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.

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