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

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9645))

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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.

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Notes

  1. 1.

    http://recsys.acm.org/recsys14/session-diversity-novelty-serendipity/.

  2. 2.

    0 is a typical value out of the range of rating scale, which can be used to distinguish the rating value and the rating behavior.

  3. 3.

    http://glinden.blogspot.com/2006/03/early-amazon-similarities.html.

  4. 4.

    Types of implicit feedback include rating behaviors, purchase history, browsing history, and search patterns.

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Acknowledgements

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

Author information

Authors and Affiliations

  1. Beijing Research Center for Information Technology in Agriculture, Beijing, China

    Xiangyu Zhao & Zhongqiang Liu

  2. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China

    Wei Chen

  3. National Engineering Research Center for Information Technology in Agriculture, Beijing, China

    Xiangyu Zhao & Feng Yang

  4. Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing, China

    Wei Chen

  5. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China

    Feng Yang

  6. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing, China

    Zhongqiang Liu

Authors
  1. Xiangyu Zhao
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  2. Wei Chen
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  3. Feng Yang
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  4. Zhongqiang Liu
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Corresponding author

Correspondence to Wei Chen .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Hong Gao

  2. Kangwon National University, Kangwon, Korea (Republic of)

    Jinho Kim

  3. Kumamoto University, Kumamoto-shi, Japan

    Yasushi Sakurai

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Zhao, X., Chen, W., Yang, F., Liu, Z. (2016). Improving Diversity of User-Based Two-Step Recommendation Algorithm with Popularity Normalization. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-32055-7_2

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