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.
|Titolo:||Dynamic Social Recommendation|
|Data di pubblicazione:||2017|
|Citazione:||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.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|