Diversified Interactive Recommendation with Implicit Feedback

  • Yong Liu Nanyang Technological University
  • Yingtai Xiao University of Science and Technology of China
  • Qiong Wu Nanyang Technological University
  • Chunyan Miao Nanyang Technological University
  • Juyong Zhang University of Science and Technology of China
  • Binqiang Zhao Alibaba Group
  • Haihong Tang Alibaba Group

Abstract

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC2B), for interactive recommendation with users' implicit feedback. Specifically, DC2B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC2B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning