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Determination of Attribute Weights for Recommender Systems Based on Product Popularity

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  • Kagie, M.
  • van Wezel, M.C.
  • Groenen, P.J.F.

Abstract

In content- and knowledge-based recommender systems often a measure of (dis)similarity between products is used. Frequently, this measure is based on the attributes of the products. However, which attributes are important for the users of the system remains an important question to answer. In this paper, we present two approaches to determine attribute weights in a dissimilarity measure based on product popularity. We count how many times products are sold and based on this, we create two models to determine attribute weights: a Poisson regression model and a novel boosting model minimizing Poisson deviance. We evaluate these two models in two ways, namely using a clickstream analysis on four different product catalogs and a user experiment. The clickstream analysis shows that for each product catalog the standard equal weights model is outperformed by at least one of the weighting models. The user experiment shows that users seem to have a different notion of product similarity in an experimental context.

Suggested Citation

  • Kagie, M. & van Wezel, M.C. & Groenen, P.J.F., 2009. "Determination of Attribute Weights for Recommender Systems Based on Product Popularity," ERIM Report Series Research in Management ERS-2009-022-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:15910
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    File URL: https://repub.eur.nl/pub/15910/ERS-2009-022-MKT.pdf
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    References listed on IDEAS

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    1. Kagie, M. & van Wezel, M.C. & Groenen, P.J.F., 2007. "A graphical shopping interface bases on product attributes," Econometric Institute Research Papers EI 2007-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    4. Kagie, M. & van Wezel, M.C. & Groenen, P.J.F., 2008. "Choosing Attribute Weights for Item Dissimilarity using Clikstream Data with an Application to a Product Catalog Map," ERIM Report Series Research in Management ERS-2008-024-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.
    5. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
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    Cited by:

    1. Kagie, M. & van Wezel, M.C. & Groenen, P.J.F., 2009. "An Empirical Comparison of Dissimilarity Measures for Recommender Systems," ERIM Report Series Research in Management ERS-2009-023-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.

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    More about this item

    Keywords

    attribute weights; boosting; dissimilarity measures; evaluation; poisson regression; recommender systems;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • 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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