FuseRec: fusing user and item homophily modeling with temporal recommender systems

Recommender systems can benefit from a plethora of signals influencing user behavior such as her past interactions, her social connections, as well as the similarity between different items. However, existing methods are challenged when taking all this data into account and often do not exploit all available information. This is primarily due to the fact that it is non-trivial to combine the various information as they mutually influence each other. To address this shortcoming, here, we propose a 'Fusion Recommender' (FuseRec), which models each of these factors separately and later combines them in an interpretable manner. We find this general framework to yield compelling results on all three investigated datasets, Epinions, Ciao, and CiaoDVD, outperforming the state-of-the-art by more than 14% for Ciao and Epinions. In addition, we provide a detailed ablation study, showing that our combined model achieves accurate results, often better than any of its components individually. Our model also provides insights on the importance of each of the factors in different datasets.

Automated summary

Recommender systems can benefit from a variety of signals influencing user behavior, but existing methods often fail to fully utilize all available information. The 'Fusion Recommender' model is proposed, which models different factors separately and combines them in an interpretable way. This model shows promising results across multiple datasets, outperforming other techniques by over 14% while also providing insights on the importance of each factor.