Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Overview <s> Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering. <s> BIB001 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Overview <s> Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods. <s> BIB002
Attribute-aware recommendation models are proposed to tackle the challenges of integrating additional information from user/item/rating. There are two strategies for designing attributeaware collaborative filtering-based systems. One direction is to combine content-based recommendation models with CF models, which can directly accept attributes as content to perform recommendation. On the other hand, researchers also try to extend an existing collaborative filtering algorithm such that it leverages attribute information. Rather, we will focus on four important factors in the design of an attribute-aware recommender system in current research, as shown in Figure 4 . They are specifically discussed from sections 3.2 to 3.5. With respect to input data, attribute sources determine whether an attribute vector is relevant to users, items, or ratings. For example, age describes a user instead of an item; rating time must be appended to ratings, representing when the rating event occurred. Different models impose distinct strategies to integrate attributes of specific sources. Additionally, a model may constrain attribute types that can be used. For instance, graphbased collaborative filtering realizations define attributes as node types, which is not appropriate for numerical attributes. Rating type is the factor that is emphasized by most model designers. Besides usual numerical ratings, many recommendation models concentrate on binary rating data, where the ratings represent whether users interact with items. Finally, different recommender systems emphasize different recommendation goals. One is to predict the ratings from users to items through minimizing the error between the predicted and real ratings. Another is to produce the ranking among items given a user instead of caring about the real rating value of a single item. We then summarize the design categories of all the surveyed papers in a table in section 3.6. Throughout this paper, we will use X = x 1 x 2 . . . x N ∈ R K×N to denote the attribute matrix, where each column x i represents a K-dimensional attribute vector of entity i. Here, an entity can refer to a user, an item, or a rating, determined by attribute sources (discussed in section 3.2). If attributes are limited categorically, then X ∈ {0, 1} K×N can be represented by one-hot encoding (discussed in section 3.3). Note that our survey does not include models designed specifically for a certain type of attribute but rather covers models that are general enough to accept different types of attributes. For example, Collaborative Topic Regression (CTR) BIB001 extends matrix factorization with Latent Dirichlet Allocation (LDA) to import text attributes. Social Regularization BIB002 specifically utilizes user social networks to regularize the learning of matrix factorization. Neither model is included since they are not general enough to deal with general attributes.