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Improving Diversity of User-Based Two-Step Recommendation Algorithm with Popularity Normalization

  • Xiangyu Zhao
  • Wei ChenEmail author
  • Feng Yang
  • Zhongqiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

Recommender systems become increasingly significant in solving the information overload problem. Beyond conventional rating prediction and ranking prediction recommendation technologies, two-step recommendation algorithms have been demonstrated that they have outstanding accuracy performance in top-N recommendation tasks. However, their recommendation lists are biased towards popular items. In this paper, we propose a popularity normalization method to improve the diversity of user-based two-step recommendation algorithms. Experiment results show that our proposed approach improves the diversity performance significantly while maintaining the advantage of two-step recommendation approaches on accuracy metrics.

Keywords

Recommender system Collaborative filtering Diversity Two-step recommendation Popularity normalization 

Notes

Acknowledgements

This work is supported by the National Key Technology R&D Program of China (project no. 2014BAD10B08).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiangyu Zhao
    • 1
    • 3
  • Wei Chen
    • 2
    • 4
    Email author
  • Feng Yang
    • 3
    • 5
  • Zhongqiang Liu
    • 1
    • 6
  1. 1.Beijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.Agricultural Information InstituteChinese Academy of Agricultural SciencesBeijingChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  4. 4.Key Laboratory of Agri-information Service TechnologyMinistry of AgricultureBeijingChina
  5. 5.Key Laboratory of Agri-informaticsMinistry of AgricultureBeijingChina
  6. 6.Beijing Engineering Research Center of Agricultural Internet of ThingsBeijingChina

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