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Title: Diversified interactive recommendation with implicit feedback
Authors: Liu, Yong
Xiao, Yingtai
Wu, Qiong
Miao, Chunyan
Zhang, Juyong
Zhao, Binqiang
Tang, Haihong
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Liu, Y., Xiao, Y., Wu, Q., Miao, C., Zhang, J., Zhao, B., & Tang, H. (2020). Diversified interactive recommendation with implicit feedback. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 4932-4939. doi:10.1609/aaai.v34i04.5931
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.
DOI: 10.1609/aaai.v34i04.5931
Rights: © 2020 Association for the Advancement of Artificial Intelligence (AAAI). All rights reserved. This paper was published in Proceedings of the AAAI Conference on Artificial Intelligence and is made available with permission of Association for the Advancement of Artificial Intelligence (AAAI).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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