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The purpose of the Session-Based Recommendation System is to predict the users next click according to the previous session sequence. The current studies generally learn user preferences according to the transitions of items in the users session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the users specific preferences. In this paper, we propose a heterogeneous graph neural network-based session recommendation method, named SR-HetGNN, which can learn session embeddings by heterogeneous graph neural network (HetGNN), and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, to consider the influence of users long and short-term preferences, local and global session embeddings are combined with the attentional network to obtain the final session embedding. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling u
Heterogeneous information network (HIN) is widely applied to recommendation systems due to its capability of modeling various auxiliary information with meta-path. However, existing HIN-based recommendation models usually fuse the information from va
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the users recent interest.
Predicting a users preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent research of the session-based recomm
Session-based recommendation (SBR) learns users preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful kind of way, wh