In this paper, we describe our research activity on an approach to personalized news recommendation, which captures the temporal dynamics of the active user's interests. In such recommender, the user profile explicitly involves the time dimension in representing her interests and preferences. Each user's interest is represented as a signal, thus characterizing its evolution over time. To this aim, a signal processing technique (i.e., the discrete wavelet transform) is adopted to represent and analyze such signals. Furthermore, we report the experimental results of a very preliminary comparative evaluation on an online available dataset. Such results seem encouraging, thus spurring us to continue developing our approach.
Caldarelli, S., Gurini, D.F., Micarelli, A., Sansonetti, G. (2016). A signal-based approach to news recommendation. In CEUR Workshop Proceedings. CEUR-WS.
A signal-based approach to news recommendation
Micarelli A.;Sansonetti G.
2016-01-01
Abstract
In this paper, we describe our research activity on an approach to personalized news recommendation, which captures the temporal dynamics of the active user's interests. In such recommender, the user profile explicitly involves the time dimension in representing her interests and preferences. Each user's interest is represented as a signal, thus characterizing its evolution over time. To this aim, a signal processing technique (i.e., the discrete wavelet transform) is adopted to represent and analyze such signals. Furthermore, we report the experimental results of a very preliminary comparative evaluation on an online available dataset. Such results seem encouraging, thus spurring us to continue developing our approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
