Abstract: Session recommendation predicts the next interaction item based on anonymous user interaction data over a short pe-riod of time.Sessions have characteristics such as few items and long-tail distribution of items.Existing session recommendation models based on graph contrast learning construct positive and negative samples by randomly cropping and perturbing the items within a session,etc.However,the above random exit strategy further shrinks the available items in shorter sessions.This makes the sessions more sparse and causes session interest learning bias.To this end,a graph contrast learning based multi-graph neural network for session-based recommendation method is proposed.The core idea is as follows:the model extracts item representations on item local graphs as well as item global graphs,incorporating both local and global higher-order neighborhood information of the items.Based on this,the model generates item-level session representations.Then,Session-level session representations are learned on the session-session graph.Finally,the model recursively generates positive and negative sample pairs using diffe-rent levels of conversational interest.And the discriminative nature of the session interests is enhanced by the contrast learning mechanism.Compared with the exit strategy,the proposed model preserves the complete session information and achieves true data expansion.Extensive experiments on two benchmark datasets show that the recommendation performance of the algorithm is much better than that of the mainstream baseline approach.

Key words: Session recommendation, Graph contrast learning, Graph neural networks, Session interest, Positive and negative samples