With the increasing information overload, the identification of new users really relevant to the target user becomes more and more complicated. In this paper, we propose a social recommender based on a user model that takes into account not only her interests and preferences, but also their evolution over time and actual nature. To accurately assess the effectiveness of the proposed approach, over 1,600 users were monitored for a full year, thus collecting over 2,700,000 tweets. In this way, it was possible to deeply evaluate the proposed model, also through a comparative analysis with other state-of-the-art social recommender systems.
Giammarino, D., Gurini, D.F., Micarelli, A., Sansonetti, G. (2017). Social recommendation with time and sentiment analysis. In UMAP 2017 - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp.376-380). Association for Computing Machinery, Inc [10.1145/3099023.3099104].
Social recommendation with time and sentiment analysis
Micarelli A.;Sansonetti G.
2017-01-01
Abstract
With the increasing information overload, the identification of new users really relevant to the target user becomes more and more complicated. In this paper, we propose a social recommender based on a user model that takes into account not only her interests and preferences, but also their evolution over time and actual nature. To accurately assess the effectiveness of the proposed approach, over 1,600 users were monitored for a full year, thus collecting over 2,700,000 tweets. In this way, it was possible to deeply evaluate the proposed model, also through a comparative analysis with other state-of-the-art social recommender systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
