Abstract
Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a recommendation strategy that more closely mimics the objectives of human curators. We consider multiple data sources to enhance the recommendation process, as well as the quality and diversity of the provided suggestions. Further, we pair each suggestion with an explanation that showcases why a book was recommended with the aim of easing the decision making process for the user. Empirical studies using Social Book Search data demonstrate the effectiveness of the proposed methodology.
Original language | English |
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Pages (from-to) | 37-44 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 2225 |
Publication status | Published - 2018 |
Externally published | Yes |
Event | 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2018 - Vancouver, Canada Duration: 7 Oct 2018 → 7 Oct 2018 |
Keywords
- Curation
- Decision-making
- Diversity
- Personalization
- Time series