Abstract
Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the "beyondaccuracy" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graphtheory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.
Original language | English |
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Title of host publication | ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization |
Publisher | Association for Computing Machinery, Inc |
Pages | 353-356 |
Number of pages | 4 |
ISBN (Electronic) | 9781450360210 |
DOIs | |
State | Published - 7 Jun 2019 |
Event | 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus Duration: 9 Jun 2019 → 12 Jun 2019 |
Publication series
Name | ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization |
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Conference
Conference | 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 |
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Country/Territory | Cyprus |
City | Larnaca |
Period | 9/06/19 → 12/06/19 |
Bibliographical note
Publisher Copyright:© 2019 Association for Computing Machinery.
Keywords
- Beyond accuracy metrics
- Recommender systems
- Resolving sets in a graph
- Serendipity in recommendations
- User modeling
ASJC Scopus subject areas
- Software