Effective Diversification of Multi-Carousel Book Recommendation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917089133985792 |
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| author | Wilten, Daniël Wenniger, Gideon Maillette de Buy Hommersom, Arjen Lucassen, Paul Poortman, Emiel |
| author_facet | Wilten, Daniël Wenniger, Gideon Maillette de Buy Hommersom, Arjen Lucassen, Paul Poortman, Emiel |
| contents | Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, that fall in categories such as genres and authors. However, while carousels offer structure and greater ease of navigation, they alone do not increase diversity in recommendations, while this is essential to keep users engaged. In this work we propose several approaches to effectively increase item diversity within the domain of book recommendations, on top of a collaborative filtering algorithm. These approaches are intended to improve book recommendations in the web catalogs of public libraries. Furthermore, we introduce metrics to evaluate the resulting strategies, and show that the proposed system finds a suitable balance between accuracy and beyond-accuracy aspects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effective Diversification of Multi-Carousel Book Recommendation Wilten, Daniël Wenniger, Gideon Maillette de Buy Hommersom, Arjen Lucassen, Paul Poortman, Emiel Information Retrieval Artificial Intelligence H.3.3 Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, that fall in categories such as genres and authors. However, while carousels offer structure and greater ease of navigation, they alone do not increase diversity in recommendations, while this is essential to keep users engaged. In this work we propose several approaches to effectively increase item diversity within the domain of book recommendations, on top of a collaborative filtering algorithm. These approaches are intended to improve book recommendations in the web catalogs of public libraries. Furthermore, we introduce metrics to evaluate the resulting strategies, and show that the proposed system finds a suitable balance between accuracy and beyond-accuracy aspects. |
| title | Effective Diversification of Multi-Carousel Book Recommendation |
| topic | Information Retrieval Artificial Intelligence H.3.3 |
| url | https://arxiv.org/abs/2511.14461 |