History-guided conversational recommendation

Yasser Salem, Jun Hong, Weiru Liu

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)
230 Downloads (Pure)

Abstract

Product recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders. We present a new critiquing-based approach, History-Guided Recommendation (HGR), which is capable of using the recommendation pairs (item and critique) or critiques only so far in the current recommendation session to predict the most likely product recommendations and therefore short-cut the sometimes protracted recommendation sessions in standard critiquing approaches. The HGR approach shows a significant improvement in the interactions between the user and the recommender. It also enables successfully accepted recommendations to be made much earlier in the session.
Original languageEnglish
Title of host publicationWWW Companion '14
Subtitle of host publicationProceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery (ACM)
Pages999-1004
Number of pages6
ISBN (Print)9781450327459
DOIs
Publication statusPublished - 7 Apr 2014

Structured keywords

  • Jean Golding

Keywords

  • Conversational Recommendation
  • critiquing
  • Recommender Systems

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