Deep Modeling of Social Relations for Recommendation

Authors

  • Wenqi Fan City University of Hong Kong
  • Qing Li City University of Hong Kong
  • Min Cheng City University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v32i1.12132

Keywords:

Recommender Systems, Social Relations, Rating Prediction, Deep Learning

Abstract

Social-based recommender systems have been recently proposed by incorporating social relations of users to alleviate sparsity issue of user-to-item rating data and to improve recommendation performance. Many of these social-based recommender systems linearly combine the multiplication of social features between users. However, these methods lack the ability to capture complex and intrinsic non-linear features from social relations. In this paper, we present a deep neural network based model to learn non-linear features of each user from social relations, and to integrate into probabilistic matrix factorization for rating prediction problem. Experiments demonstrate the advantages of the proposed method over state-of-the-art social-based recommender systems.

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Published

2018-04-29

How to Cite

Fan, W., Li, Q., & Cheng, M. (2018). Deep Modeling of Social Relations for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12132