Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling

摘要

Recommender systems are designed to help users find matching items from plenty of candidates in online platforms. In many online platforms, such as Yelp and Epinions, users' behaviors are constantly recorded over time, and the users also can build connections with others and share their interests. Previous recommendation methods have either modeled the dynamic interests or the dynamic social influences. A few studies have focused on the modeling of both factors, but they still have several limi-tations: 1) they fail to consider the complex items transitions among all session sequences, which can be used as a local factor to boost the performance of recommendation methods, and 2) they ignore that a user and their friends only share the same preferences in certain sessions, by keeping the friend vector unchanged for all target users at time t, and 3) they do not consider that a user's long-term preference may change with the evolution of interests.To overcome the above issues, in this paper, we propose an approach to incorporate item graph embedding and contextual friendship modeling into the recommendation task. Specifically, 1) we construct a directed item graph based on all historical session sequences and utilize a graph neural network to capture the rich local dependency between items, and 2) take a session-level attention mechanism to get each friend's representation according to the target user's current interests, and 3) apply max-pooling on the target user's historical session interests to learn the dynamics of his/her long-term interests. Extensive experiments on two real-world datasets show that our proposed model outperforms stateof-the-art methods consistently on various evaluation metrics. (c) 2020 Elsevier B.V. All rights reserved.