Group-to-group Recommendation with Neural Graph Matching

Nowadays, with the development of recommender systems, an emerging recommendation scenario called group-to-group recommendation has played a vital role in information acquisition for users. The new recommendation scenario seeks to recommend a group of related items to users with similar interests. To some extent, it alleviates the problem of point-to-point recommendations getting trapped in an information cocoon due to an over-reliance on user behaviors. For the new recommendation scenario, the existing recommendation methods cannot model the complex interactions between user groups and item groups, thus affecting the accuracy of the group-to-group recommendation. In this paper, we propose a group-to-group recommendation method, which abstracts user groups and item groups into graphs and calculates the similarity between two graphs based on graph matching, dubbed as GMRec. Specifically, we construct the graph of user groups and item groups and then calculate the graph similarity scores between user groups and item groups from two perspectives of feature matching and structure matching. Experimental results show that our model achieves higher accuracy than state-of-the-art models on three industrial datasets with different group sizes, with a maximum improvement of 8.2\%.