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Cosine based latent factor model for ranking the recommendation

Author

Listed:
  • Bipul Kumar

    (Indian Institute of Management Ranchi)

  • Pradip Kumar Bala

    (Indian Institute of Management Ranchi)

Abstract

The purpose of this paper is to propose a novel latent factor model that generates a ranked list of items in the recommendation list based on prior interaction with system on e-commerce platforms. The ranking of items in recommendation list is exhibited as an optimization model that optimizes the ranking metrics. The latent features of user and items are learnt using cosine based latent factor model which in turn are used to learn the ranking metric. This paper proposes cosine based latent factor model to learn the implicit features, and corresponding surrogate ranking loss function is optimized. Comprehensive evaluation on three benchmark datasets shows the considerable improvement of the proposed model on ranking metric.

Suggested Citation

  • Bipul Kumar & Pradip Kumar Bala, 2020. "Cosine based latent factor model for ranking the recommendation," Operational Research, Springer, vol. 20(1), pages 297-317, March.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:1:d:10.1007_s12351-017-0325-6
    DOI: 10.1007/s12351-017-0325-6
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    References listed on IDEAS

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    1. Kagie, M. & van der Loos, M.J.H.M. & van Wezel, M.C., 2008. "Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering," ERIM Report Series Research in Management ERS-2008-053-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Min Gao & Kecheng Liu & Zhongfu Wu, 2010. "Personalisation in web computing and informatics: Theories, techniques, applications, and future research," Information Systems Frontiers, Springer, vol. 12(5), pages 607-629, November.
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