Learning edge importance in bipartite graph-based recommendations

Abstract

In this work, we propose the P3 Learning to Rank (P3LTR) model, a generalization of the RP3Beta graph-based recommendation method. In our approach, we learn the importance of user-item relations based on features that are usually available in online recommendations (such as types of user-item past interactions and timestamps). We keep the simplicity and explainability of RP3Beta predictions. We report the improvements of P3LTR over RP3Beta on the OLX Jobs Interactions dataset, which we published.