ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development

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A Method on Long Tail Recommendation Based on Three-Factor Probabilistic Graphical Model

Feng Chenjiao1,2, Song Peng3, Wang Zhiqiang1, Liang Jiye1   

  1. 1(Key Laboratory of Computation Intelligence & Chinese Information Processing (Shanxi University), Ministry of Education, Taiyuan 030006)

    2(College of Applied Mathematics, Shanxi University of Finance and Economics, Taiyuan 030006)

    3(School of Economics and Management, Shanxi University, Taiyuan 030006)

  • Online:2021-02-05
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61876103, 61906111), the Projects of Key Research and Development Plan of Shanxi Province(201903D121162), the 1331 Engineering Project of Shanxi Province, and the Open Fund of Key Laboratory of Shanxi Province(CICIP2020005).

Abstract: In the Internet era, the explosive rise of information and unprecedented data size has greatly exceeded the receivers’ receiving and handling capability. Accordingly it has become a necessity to capture important and useful information from the massive and complex data. The problem of information overload calls for a more efficient information filtering system and this explains the birth and rise of the recommendation system. In the real recommendation situations, the long tail phenomenon is typically represented, whether in the customers’ rating or their frequency of opting for the items. Actually a deep analysis on the long tail phenomenon helps to promote the e-commerce stakeholders’ performance as well as explore the customers’ preferences, which explains the increasing attention paid on long tail recommendation. To handle the interpretability of long tail recommendation, a three-factor probabilistic graphical model is proposed. To meet the recommendation system and users’ need for interpretable item recommendation in reality, the author proposes a three-factor probabilistic graphical recommendation method basing on customers’ activity level, the items’unpopularity and customer-item preference level. In this method, the advantage of probabilistic graphical model in interpreting cause-result relationship is utilized and recommendation accuracy and design novelty in the algorithm can be ensured in this study. The results of experiments indicate that with the advantage of interpretability, the three-factor probabilistic graphical recommendation method can ensure prediction accuracy and perform better in providing novel recommendations. 

Key words: recommender system, long tail, probabilistic graphical model, variational inference; novelty