TY  - JOUR
AV  - public
TI  - Adaptive social recommendation in a multiple category landscape
ID  - eprints2847
IS  - 2
UR  - http://dx.doi.org/10.1140/epjb/e2012-30899-9
JF  - The European Physical Journal B - Condensed Matter
N2  - People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users? preferences. In this work we introduce a more realistic assumption that users? tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users? taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations? diversity while preserving their accuracy.
KW  - Statistical and Nonlinear Physics
PB  - Springer
VL  - 86
SN  - 1434-6028
Y1  - 2013///
A1  - Chen, Duanbing
A1  - Zeng, An
A1  - Cimini, Giulio
A1  - Zhang, Yi-Cheng
ER  -