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Graph Based Hybrid Approach for Long-Tail Item Recommendation in Collaborative Filtering

Published:02 January 2021Publication History

ABSTRACT

Recommender system plays a vital role in e-commerce business by providing personalized product recommendation. However, most of the existing recommender system are accuracy-centric and biases on recommending popular items. However, recommending relevant long tail items is another research challenge in recommendation community. In this article, we propose a graph based approach to enhance the long tail items in recommendation. The preliminary results are very encouraging on standard rating dataset. Proposed approach outperforms recently introduced recommender systems which focus on long tail item recommendation.

References

  1. [1] R Liu, Z Jin.(2015). An Improved Graph-based Recommender System for Finding Novel Recommendations among Relevant Items. Atlantis Press. In2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 Dec.Google ScholarGoogle Scholar
  2. [2] Y Koren, R Bell, C Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42,8(2009),30-37.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] K Lee, K Lee.(2015). Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items. Expert Systems with Applications 42,10(2015), 4851-4858.Google ScholarGoogle ScholarDigital LibraryDigital Library

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    CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
    January 2021
    453 pages

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 2 January 2021

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    • extended-abstract
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate197of680submissions,29%

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