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Proceedings of the 7th International Conference on Emerging Databases pp 214–220Cite as

A Method to Maintain Item Recommendation Equality Among Equivalent Items in Recommender Systems

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 461)

Abstract

Collaborative Filtering is a useful algorithm to offer personalized recommendations for users. However, there are several technical challenges in collaborative filtering, including the first-rater problem, where an item not yet evaluated cannot be recommended until it has been rated. In the paper, the presenting method deals with the first-rater problem that is similar to the process starvation is operating systems. The method reduces the score gap between items and makes it possible for a new item or an item with no user preference to be recommended automatically. Thus, the system can recommend items in the same group without bias. Finally, we present an analysis of an example of the algorithm.

Keywords

  • Recommender systems
  • Collaborative filtering
  • First-Rater problem
  • Data sparsity

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Acknowledgement

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0115-16-1009, Development of smart learning interaction contents for acquiring foreign languages through experiential awareness).

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Authors and Affiliations

  1. Department of IT Engineering, Sookmyung Women’s University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul, 04310, South Korea

    Yeo-jin Hong, Shineun Lee & Young-ho Park

Authors
  1. Yeo-jin Hong
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  2. Shineun Lee
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  3. Young-ho Park
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Corresponding author

Correspondence to Young-ho Park .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Inha University, Incheon, Korea (Republic of)

    Wookey Lee

  2. Department of Information and Communication Engineering, Inha University, Incheon, Korea (Republic of)

    Prof. Wonik Choi

  3. Department of Computer Science and Engineering, Sogang University, Seoul, Korea (Republic of)

    Prof. Sungwon Jung

  4. Department of Library and Information Science, Yonsei University, Seoul, Korea (Republic of)

    Prof. Min Song

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Hong, Yj., Lee, S., Park, Yh. (2018). A Method to Maintain Item Recommendation Equality Among Equivalent Items in Recommender Systems. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_22

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  • DOI: https://doi.org/10.1007/978-981-10-6520-0_22

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