Abstract
Recommender systems use nowadays more and more data about users and items as part of the recommendation process. The availability of auxiliary data, going beyond the mere user/item data, has the potential to improve recommendations. In this work we examine the contribution of two types of social auxiliary data – namely, tags and friendship links – to the accuracy of a graph-based recommender. We measure the impact of the availability of auxiliary data on the recommendations using features extracted from both the auxiliary and the original data. The evaluation shows that the social auxiliary data improves the accuracy of the recommendations, and that the greatest improvement is achieved when graph features mirroring the nature of the auxiliary data are extracted by the recommender.
Original language | English |
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Title of host publication | User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings |
Editors | Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben |
Publisher | Springer Verlag |
Pages | 447-458 |
Number of pages | 12 |
ISBN (Electronic) | 9783319087856 |
DOIs | |
State | Published - 2014 |
Event | 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Netherlands Duration: 7 Jul 2014 → 11 Jul 2014 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 8538 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 |
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Country/Territory | Netherlands |
City | Aalborg |
Period | 7/07/14 → 11/07/14 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2014.
Keywords
- Feature extraction
- Graph-based recommendations
- Music recommendations
- Social data
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science