Numerical facet range partition: Evaluation metric and methods

Xueqing Liu, Chengxiang Zhai, Wei Han, Onur Gungor

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

3 Scopus citations

Abstract

Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
Pages662-671
Number of pages10
ISBN (Electronic)9781450349147
DOIs
StatePublished - 2017
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Publication series

Name26th International World Wide Web Conference 2017, WWW 2017 Companion

Conference

Conference26th International World Wide Web Conference, WWW 2017 Companion
Country/TerritoryAustralia
CityPerth
Period3/04/177/04/17

Keywords

  • Faceted search
  • Information retrieval models
  • Non-smooth optimization
  • User search log

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