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Session-based recommendation systems have high application value. Determining how to make better use of anonymous user sessions to recommend items of interest is a considerable challenge for current recommendation systems. Existing research has mainly focused on sequential session patterns; however, due to the complexity and diversity of user interests, such interests cannot be effectively modeled in this way. Therefore, in this paper, we investigate the transition patterns between items by constructing a session graph and propose a novel model called Weighted Graph Interest Networks (WGIN) that collaboratively considers hidden user preference information and the potential order of items in the session graph for a session-based recommendation system. Specifically, we propose a repetitive weighted graph neural network (RWGNN), which pays attention to the transitions between frequent items in a session to deeply explore the preferences of users. In addition, we establish a new Transformer structure to model long-term and short-term user preferences and obtain rich session embeddings. Extensive experiments on two real datasets illustrate that the proposed model outperforms other state-of-the-art session-based recommendation methods.

WGIN: A Session-Based Recommendation Model Considering the Repeated Link Effect