Adaptive social recommendation in a multiple category landscape

TL;DR

This paper investigates adaptive social recommendation systems with multi-vector user tastes, proposing new similarity measures to improve recommendation accuracy and discussing ways to enhance diversity without losing precision.

Contribution

It introduces a realistic multi-vector user taste model and develops novel similarity measures that significantly improve recommendation precision in social networks.

Findings

Multi-vector user taste model reduces recommendation accuracy.

New similarity measures improve precision substantially.

Balancing diversity and accuracy remains a key challenge.

Abstract

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.