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
Hauptverfasser: Sansonetti, Giuseppe, Gurini, Davide Feltoni, Gasparetti, Fabio, Micarelli, Alessandro
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
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ISBN:1450349935, 9781450349932
ISSN:2473-991X
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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.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3110149