ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Denis Kotkov, Zhao Qian, Launis Kati, Mats Neovius

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
7 Downloads (Pure)

Abstract

Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

Original languageEnglish
Title of host publicationRecSys '20: Fourteenth ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Pages432–437
Number of pages6
ISBN (Electronic)9781450375832
ISBN (Print)9781450375832
DOIs
Publication statusPublished - 22 Sep 2020
MoE publication typeA4 Article in a conference publication
Eventconference; 2020-09-22; 2020-09-26 - Fourteenth ACM Conference on Recommender Systems
Duration: 22 Sep 202026 Sep 2020

Conference

Conferenceconference; 2020-09-22; 2020-09-26
Period22/09/2026/09/20

Keywords

  • conversational recommender systems
  • critiquing recommender systems
  • information exploration tool
  • interactive recommendation
  • recommender systems
  • related item recommendations
  • user control
  • user interfaces

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