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Adaptive ensemble with trust networks and collaborative recommendations

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Abstract

Several existing recommender algorithms combine collaborative filtering and social/trust networks together in order to overcome the problems caused by data scarcity and to produce more effective recommendations for users. In general, those methods fuse a user’s own taste and his trusted friends/users’ tastes using an ensemble model where a parameter is used to balance these two components. However, this parameter is often set as a constant and with no regard to users’ individual characteristics. Aiming at introducing personalization to solve the above problem, we propose a local topology-based ensemble model to adaptively combine a user’s own taste and his trusted friends/users’ tastes. We take users’ clustering coefficients in the social/trust networks as the indicator to measure the consistence of their selecting trusted friends/users and leverage this local topology-based parameter in the ensemble model. To predict the likelihood of users’ purchasing actions on items, we also combine item ratings and sentiment values which are reflected in the review contents as the input to the adaptive ensemble model. We conduct comprehensive experiments which demonstrate the superiority of our adaptive algorithms over the existing ones.

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Notes

  1. http://www.amazon.com.

  2. http://www.epinions.com.

  3. http://www.flixster.com.

  4. http://ciao.com.

  5. http://www.cs.sfu.ca/~sja25/personal/datasets/.

  6. http://www.public.asu.edu/~jtang20/datasetcode/truststudy.htm.

  7. http://www.kdd.org/conferences/kdd-2011-san-diego-ca-august-21-24-2011.

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Acknowledgments

Haitao Zou and Zhiguo Gong were supported in part by Fund of Science and Technology Development of Macau Government under FDCT/106/2012/A3 and FDCT/116/2013/A3 and in part by University Macau Research Committee under MYRG188-FST11-GZG and MYRG105-FST13-GZG. Nan Zhang was supported in part by the National Science Foundation under Grants 0852674, 0915834, 1117297, and 1343976. Any opinions, findings, conclusions, and/or recommendations expressed in this material, either expressed or implied, are those of the authors and do not necessarily reflect the views of the sponsors listed above.

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Authors and Affiliations

  1. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China

    Haitao Zou & Zhiguo Gong

  2. Department of Computer Science, George Washington University, Washington, DC, USA

    Nan Zhang

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qing Li & Yanghui Rao

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  1. Haitao Zou
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  2. Zhiguo Gong
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  3. Nan Zhang
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  4. Qing Li
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  5. Yanghui Rao
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Correspondence to Zhiguo Gong.

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Zou, H., Gong, Z., Zhang, N. et al. Adaptive ensemble with trust networks and collaborative recommendations. Knowl Inf Syst 44, 663–688 (2015). https://doi.org/10.1007/s10115-014-0782-7

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