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A probabilistic inference model for recommender systems

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Abstract

Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user’s preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness.

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Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, Beijing, 100124, China

    Jiajin Huang, Kunlei Zhu & Ning Zhong

  2. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 371-0816, Japan

    Ning Zhong

Authors
  1. Jiajin Huang
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  2. Kunlei Zhu
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  3. Ning Zhong
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Correspondence to Ning Zhong.

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Huang, J., Zhu, K. & Zhong, N. A probabilistic inference model for recommender systems. Appl Intell 45, 686–694 (2016). https://doi.org/10.1007/s10489-016-0783-1

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  • DOI: https://doi.org/10.1007/s10489-016-0783-1

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