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Modeling Implicit Communities in Recommender Systems

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10570))

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Abstract

In recommender systems, a group of users may have similar preferences on a set of items. As the groups of users and items are not explicitly given, these similar-preferences groups are called implicit communities (where users inside same communities may not necessarily know each other).

Implicit communities can be detected with users’ rating behaviors. In this paper, we propose a unified model to discover the implicit communities with rating behaviors from recommender systems.

Following the spirit of Latent Factor Model, we design a bayesian probabilistic graphical model which generates the implicit communities, where the latent vectors of users/items inside the same community follow the same distribution. An implicit community model is proposed based on rating behaviors and a Gibbs Sampling based algorithm is proposed for corresponding parameter inferences. To the best of our knowledge, this is the first attempt to integrate the rating information into implicit communities for recommendation.

We provide a linear model (matrix factorization based) and a non-linear model (deep neural network based) for community modeling in recsys.

Extensive experiments on seven real-world datasets have been conducted in comparison with 14 state-of-art recommendation algorithms. Statistically significant improvements verify the effectiveness of the proposed implicit community based models. They also show superior performances in cold-start scenarios, which contributes to the application of real-life recommender systems.

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Notes

  1. 1.

    In all of the following result tables, \(*\) represents the improvements of ICR are statically significant with \(p<0.05\).

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Acknowledgement

This work is supported in part by China Grant U1636215, 61572492, and the Hong Kong Scholars Program.

Author information

Authors and Affiliations

  1. Institute of Interdisciplinary Information Sciences, Tsinghua University, Beijing, China

    Lin Xiao

  2. Department of Computer Science, GuangZhou Univeristy and The University of HongKong, Hongkong, China

    Gu Zhaoquan

Authors
  1. Lin Xiao
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  2. Gu Zhaoquan
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Corresponding author

Correspondence to Lin Xiao .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Xiao, L., Zhaoquan, G. (2017). Modeling Implicit Communities in Recommender Systems. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-68786-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68785-8

  • Online ISBN: 978-3-319-68786-5

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