Facet selection algorithms for web product search

Damir Vandic, Flavius Frasincar, Uzay Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

36 Citations (Scopus)


Multifaceted search is a commonly used interaction paradigm in e-commerce applications, such as Web shops. Because of the large amount of possible product attributes, Web shops usually make use of static information to determine which facets should be displayed. Unfortunately, this approach does not take into account the user query, leading to a nonoptimal facet drill down process. In this paper, we focus on automatic facet selection, with the goal of minimizing the number of steps needed to find the desired product. We propose several algorithms for facet selection, which we evaluate against the state-of-the-art algorithms from the literature. We implement our approach in a Web application called faccy.net. The evaluation is based on simulations employing 1000 queries, 980 products, 487 facets, and three drill down strategies. As evaluation metrics we use the average number of clicks, the average utility, and the top-10 promotion percentage. The results show that the Probabilistic Entropy algorithm significantly outperforms the other considered algorithms.

Original languageEnglish
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Print)9781450322638
Publication statusPublished - 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 27 Oct 20131 Nov 2013


Conference22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA


  • Facet selection
  • Information retrieval
  • Product search


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