Adaptive social recommendation in a multiple category landscape
This work addresses the challenge of personalized recommendations in social networks for users facing information overload, representing an incremental improvement by refining similarity measures within an existing framework.
The paper tackles the problem of information overload by proposing adaptive social recommendation under a more realistic assumption of multiple taste vectors, showing that this framework initially leads to poor outcomes but can be improved with novel similarity measures to enhance precision.
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.