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.
Sansonetti, G., FELTONI GURINI, D., Gasparetti, F., Micarelli, A. (2017). Dynamic Social Recommendation. In Proceeding ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp.943-947). ACM [10.1145/3110025.3110149].
Dynamic Social Recommendation
SANSONETTI, GIUSEPPE;Davide Feltoni Gurini;Fabio Gasparetti;and Alessandro Micarelli
2017-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
