  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
and 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.
