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Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights

In: Innovation Through Information Systems

Author

Listed:
  • Nicolas Haubner

    (Karlsruhe Institute of Technology)

  • Thomas Setzer

    (Catholic University of Eichstätt-Ingolstadt)

Abstract

Recommender systems (RS) play a key role in e-commerce by pre-selecting presumably interesting products for customers. Hybrid RSs using a weighted average of individual RSs’ predictions have been widely adopted for improving accuracy and robustness over individual RSs. While for regression tasks, approaches to estimate optimal weighting schemes based on individual RSs’ out-of-sample errors exist, there is scant literature in classification settings. Class prediction is important for RSs in e-commerce, as here item purchases are to be predicted. We propose a method for estimating weighting schemes to combine classifying RSs based on the variance-covariance structures of the errors of individual models’ probability scores. We evaluate the approach on a large real-world e-commerce data set from a European telecommunications provider, where it shows superior accuracy compared to the best individual model as well as a weighting scheme that averages the predictions using equal weights.

Suggested Citation

  • Nicolas Haubner & Thomas Setzer, 2021. "Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 56-71, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_4
    DOI: 10.1007/978-3-030-86797-3_4
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