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

Denis Kotkov, Zhao Qian, Launis Kati, Mats Neovius

Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

1 Citeringar (Scopus)
7 Nedladdningar (Pure)


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.

Titel på gästpublikationRecSys '20: Fourteenth ACM Conference on Recommender Systems
FörlagAssociation for Computing Machinery
Antal sidor6
ISBN (elektroniskt)9781450375832
ISBN (tryckt)9781450375832
StatusPublicerad - 22 sep 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemangconference; 2020-09-22; 2020-09-26 - Fourteenth ACM Conference on Recommender Systems
Varaktighet: 22 sep 202026 sep 2020


Konferensconference; 2020-09-22; 2020-09-26


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


Fördjupa i forskningsämnen för ”ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här