Multi-behavior Recommendation with Graph Convolutional Networks

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks and favourites, can serve as an effective solution. Early efforts towards multi-behavior recommendation fail to capture behaviors' different influence strength on target behavior. They also ignore behaviors' semantics which is implied in multi-behavior data. Both of these two limitations make the data not fully exploited for improving the recommendation performance on the target behavior.

In this work, we approach this problem by innovatively constructing a unified graph to represent multi-behavior data and proposing a new model named MBGCN (short for Multi-Behavior Graph Convolutional Network). Learning behavior strength by useritem propagation layer and capturing behavior semantics by itemitem propagation layer, MBGCN can well address the limitations of existing works. Empirical results on two real-world datasets verify the effectiveness of our model in exploiting multi-behavior data. Our model outperforms the best baseline by 25.02% and 6.51% averagely on two datasets. Further studies on cold-start users confirm the practicability of our proposed model.