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Integrating heterogeneous information via flexible regularization framework for recommendation

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Abstract

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommender system as a heterogeneous information network and introduce meta-path-based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization-based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting users’ and items’ similarities as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impacts on users and items.

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Notes

  1. http://movie.douban.com/.

  2. http://grouplens.org/datasets/movielens/.

  3. http://www.yelp.com/dataset_challenge/.

  4. http://book.douban.com/.

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Acknowledgments

This work is supported by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), the National Natural Science Foundation of China (No. 71231002, 61473273), the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education, US NSF through grants III-1526499, and 2015 Microsoft Research Asia Collaborative Research Program.

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Authors and Affiliations

  1. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

    Chuan Shi, Jian Liu & Bin Wu

  2. The Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Fuzhen Zhuang

  3. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

Authors
  1. Chuan Shi
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  2. Jian Liu
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  3. Fuzhen Zhuang
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  4. Philip S. Yu
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  5. Bin Wu
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Correspondence to Fuzhen Zhuang.

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Shi, C., Liu, J., Zhuang, F. et al. Integrating heterogeneous information via flexible regularization framework for recommendation. Knowl Inf Syst 49, 835–859 (2016). https://doi.org/10.1007/s10115-016-0925-0

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  • DOI: https://doi.org/10.1007/s10115-016-0925-0

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