Deep Modeling of Group Preferences for Group-Based Recommendation

Authors

  • Liang Hu Shanghai Jiaotong University
  • Jian Cao Shanghai Jiaotong University
  • Guandong Xu University of Technology Sydney
  • Longbing Cao University of Technology Sydney
  • Zhiping Gu Shanghai Technical Institute of Electronics & Information
  • Wei Cao University of Technology Sydney

DOI:

https://doi.org/10.1609/aaai.v28i1.9007

Keywords:

group recommender system, feature learning, deep learning, restricted boltzmann machine, deep belief net

Abstract

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.

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Published

2014-06-21

How to Cite

Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., & Cao, W. (2014). Deep Modeling of Group Preferences for Group-Based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.9007

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Section

Main Track: Novel Machine Learning Algorithms