Contrastive Graph Learning for Session-based Recommendation
Yan Chen, Dongqin Liu, Yipeng Su, Yan Zhou, Jizhong Han, Ruixuan Li · 2022
In view of the problem that random deletion of graph nodes and edges may delete important item nodes when constructing graph structure enhancement samples in existing research work, which is not conducive to item node learning, this paper proposes a session-based recommendation method based on graph structure information enhancement. According to the importance of the nodes in the graph, the enhanced samples on the graph structure are constructed to enrich the representation of the item nodes. Firstly, our method constructs an item transfer graph according to the sequence of items interacted by the user, and then calculate the importance of the nodes and edges in the item transfer graph according to the in-degree and out-degree information of the user nodes in the graph. For important nodes and edges, we delete them with small probability. For the enhanced graph structure, we design contrastive learning on the graph structure to learn the representations of nodes on the graph. Finally, in the traditional conversation recommendation task, the auxiliary task of contrastive learning is added, and the multi-task learning framework is applied to learn the user's preference in session-based recommendation. Experimental results show that this method can effectively improve the performance of session sequence recommendation.