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.

Original languageEnglish (US)
Pages (from-to)837-862
Number of pages26
JournalData Mining and Knowledge Discovery
Issue number3
StatePublished - May 2021


  • Attention-based graph networks
  • Item similarity modeling
  • Social recommendation
  • Temporal recommender systems

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications


Dive into the research topics of 'FuseRec: fusing user and item homophily modeling with temporal recommender systems'. Together they form a unique fingerprint.

Cite this