KGAT: Knowledge Graph Attention Network for Recommendation

“The success of recommendation system makes it prevalent in Web applications, ranging from search engines, E-commerce, to social media sites and news portals — without exaggeration, almost every service that provides content to users is equipped with a recommendation system. To predict user preference from the key (and widely available) source of user behavior data, much research effort has been devoted to collaborative filtering (CF) [12, 13, 32]. Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information [30, 31], such as item attributes, user profiles, and contexts, thus perform poorly in sparse situations where users and items have few interactions. To integrate such information, a common paradigm is to transform them into a generic feature vector, together with user ID and item ID, and feed them into a supervised learning (SL) model to predict the score. Such a SL paradigm for recommendation has been widely deployed in industry [7, 24, 41], and some representative models include factorization machine (FM) [23], NFM (neural FM) [11], Wide&Deep [7], and xDeepFM [18], etc.

Although these methods have provided strong performance, a deficiency is that they model each interaction as an independent data instance and do not consider their relations. This makes them insufficient to distill attribute-based collaborative signal from the collective behaviors of users. As shown in Figure 1, there is an interaction between user u1 and movie i1, which is directed by the person e1. CF methods focus on the histories of similar users who also watched i1, i.e., u4 and u5; while SL methods emphasize the similar items with the attribute e1, i.e., i2. Obviously, these two types of information not only are complementary for recommendation but also form a high-order relationship between a target user and item together. However, existing SL methods fail to unify them and cannot take into account the high-order connectivity, such as the users in the yellow circle who watched other movies directed by the same person e1, or the items in the grey circle that share other common relations with e1.

To address the limitation of feature-based SL models, a solution is to take the graph of item side information, aka. knowledge graph1 [3, 4], into account to construct the predictive model. We term the hybrid structure of knowledge graph and user-item graph as collaborative knowledge graph (CKG).”