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 language | English |
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Title of host publication | RecSys '20: Fourteenth ACM Conference on Recommender Systems |
Publisher | Association for Computing Machinery |
Pages | 432–437 |
Number of pages | 6 |
ISBN (Electronic) | 9781450375832 |
ISBN (Print) | 9781450375832 |
DOIs | |
Publication status | Published - 22 Sep 2020 |
MoE publication type | A4 Article in a conference publication |
Event | conference; 2020-09-22; 2020-09-26 - Fourteenth ACM Conference on Recommender Systems Duration: 22 Sep 2020 → 26 Sep 2020 |
Conference
Conference | conference; 2020-09-22; 2020-09-26 |
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Period | 22/09/20 → 26/09/20 |
Keywords
- conversational recommender systems
- critiquing recommender systems
- information exploration tool
- interactive recommendation
- recommender systems
- related item recommendations
- user control
- user interfaces