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Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

Author

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  • Kagie, M.
  • van der Loos, M.J.H.M.
  • van Wezel, M.C.

Abstract

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.

Suggested Citation

  • 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.
    • Handle: RePEc:ems:eureri:13180
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    File URL: https://repub.eur.nl/pub/13180/ERS-2008-053-MKT.pdf
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    References listed on IDEAS

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    6. Wayne S. DeSarbo & C. Anthony Di Benedetto & Michael Song & Indrajit Sinha, 2005. "Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types, capabilities, environmental uncertainty, and firm performance," Strategic Management Journal, Wiley Blackwell, vol. 26(1), pages 47-74, January.
    7. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    algorithms; hybrid recommender systems; probabilistic latent semantic analysis; recommender systems;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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