Abstract
This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from Rotten Tomatoes and Audio CDs from Amazon Products. Our results show that the text-based user-kNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE.
Original language | English |
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Title of host publication | User Modeling, Adaptation, and Personalization |
Subtitle of host publication | 22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings |
Editors | Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 195-206 |
Number of pages | 12 |
ISBN (Electronic) | 9783319087856 |
ISBN (Print) | 9783319087863 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014 - Aalborg, Netherlands Duration: 7 Jul 2014 → 11 Jul 2014 Conference number: 22nd https://link.springer.com/book/10.1007/978-3-319-08786-3 (Conference Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 8538 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014 |
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Abbreviated title | UMAP 2014 |
Country/Territory | Netherlands |
City | Aalborg |
Period | 7/07/14 → 11/07/14 |
Internet address |
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Keywords
- Collaborative Filtering
- Recommender systems
- Semantic similarity measures
- Text reviews