Graph Fusion in Reciprocal Recommender Systems

Abstract

Unlike traditional user-item recommendation tasks (e.g., movie or consumer-product recommendation), reciprocal recommender systems (RRSs) (e.g., online dating services and job-recruitment sites) must consider the interests of both two users. Pair matching prediction can improve the efficiency with which RRSs match potential partners. Graph Neural Networks (GNNs) are powerful models for learning representations of attributed graphs and information circulation between nodes. GNNs greatly facilitate link prediction in the area of user-item recommender systems but have not been extensively applied to RRS. In this study, we present a novel method for pair matching prediction that learns the reciprocal information circulation between users: not only side information about them but also structural information about their behavior histories. In contrast to earlier RRSs, which focus on response prediction, ours predicts both send and reply signals. Moreover, we introduce negative sample mining to explore the effect of different types of multiple samples on recommendation accuracy in real applications. Testing our method on data provided by an online dating service, we achieved an AUC of 73.15% (an absolute improvement of over 3.20% point above baseline) and an AP of 26.01% (an absolute improvement of over 2.79%) on send prediction; an AUC of 68.95% (an absolute improvement of over 1.74%) and an AP of 23.02% (an absolute improvement of over 0.70%) on reply prediction; an AUC of 71.26% (over 4.35% point absolute improvement) and an AP of 23.95% (over 0.30% point absolute improvement) on fusion reciprocal prediction.