HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation


  • Liang Hu University of Technology, Sydney
  • Songlei Jian University of Technology, Sydney
  • Longbing Cao University of Technology Sydney
  • Zhiping Gu Shanghai Technical Institute of Electronics Information
  • Qingkui Chen University of Shanghai for Science and Technology
  • Artak Amirbekyan Commonwealth Bank




Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.




How to Cite

Hu, L., Jian, S., Cao, L., Gu, Z., Chen, Q., & Amirbekyan, A. (2019). HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3830-3837. https://doi.org/10.1609/aaai.v33i01.33013830



AAAI Technical Track: Machine Learning