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Grocery Product Recommendations from Natural Language Inputs

  • Petteri Nurmi
  • Andreas Forsblom
  • Patrik Floréen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)

Abstract

Shopping lists play a central role in grocery shopping. Among other things, shopping lists serve as memory aids and as a tool for budgeting. More interestingly, shopping lists serve as an expression and indication of customer needs and interests. Accordingly, shopping lists can be used as an input for recommendation techniques. In this paper we describe a methodology for making recommendations about additional products to purchase using items on the user’s shopping list. As shopping list entries seldom correspond to products, we first use information retrieval techniques to map the shopping list entries into candidate products. Association rules are used to generate recommendations based on the candidate products. We evaluate the usefulness and interestingness of the recommendations in a user study.

Keywords

Association Rule Association Rule Mining Retrieval Result Normalize Discount Cumulative Gain Shopping List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Petteri Nurmi
    • 1
  • Andreas Forsblom
    • 1
  • Patrik Floréen
    • 1
  1. 1.Helsinki Institute for Information Technology HIITUniversity of HelsinkiFinland

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