Dynamic Social Recommendation
This paper describes a preliminary investigation of a user modeling approach, named bag-of-signals, able to take into account how user's interests evolve over time. The basic idea underlying such an approach is to model each potential user's interest as a signal. In order to represent and...
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| Veröffentlicht in: | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 943 - 947 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY, USA
ACM
31.07.2017
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| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing theory, concepts and paradigms
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing theory, concepts and paradigms
> Social recommendation
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| ISBN: | 1450349935, 9781450349932 |
| ISSN: | 2473-991X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper describes a preliminary investigation of a user modeling approach, named bag-of-signals, able to take into account how user's interests evolve over time. The basic idea underlying such an approach is to model each potential user's interest as a signal. In order to represent and analyze such signals, we make use of the wavelet transform, a signal processing technique that offers higher performance compared to other mathematical tools for non-stationary signals. As a case study, we employ and evaluate the proposed model in a recommender system of new users to follow in social media, focusing on Twitter. A comparative analysis on real-user data with some state-of-the-art techniques - some of which considering temporal effects as well - reveals the benefits of the proposed user modeling approach for personalized recommendations. |
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| ISBN: | 1450349935 9781450349932 |
| ISSN: | 2473-991X |
| DOI: | 10.1145/3110025.3110149 |

