TY  - JOUR
Y1  - 2013///
IS  - 04n05
N2  - In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope ? who thus act as selective filters in social recommendation.
VL  - 16
TI  - The Role of Taste Affinity in Agent-Based Models for Social Recommendation
A1  - Cimini, Giulio
A1  - Zeng, An
A1  - Medo, Matú?
A1  - Chen, Duanbing
JF  - Advances in Complex Systems
ID  - eprints2846
KW  - Taste similarity
KW  -  Agent-based modeling
KW  -  Adaptive complex networks
KW  -  Information diffusion and filtering
KW  -  Social recommendation
PB  - World Scientific Publishing
UR  - http://www.worldscientific.com/doi/abs/10.1142/S0219525913500094
AV  - public
SN  - 0219-5259
ER  -