Actively Building Private Recommender Networks for Evolving Reliable Relationships

Recommender systems have been successfully using information from social networks to improve the quality of results for the targeted users. In this work, we propose a novel model that allows users to actively cultivate their recommender network. Building on existing recommender systems, we suggest providing users with transparent information on users who might be able to suggest relevant items to their taste. Ensuring that users may keep their desired privacy level, this framework allows users to make anonymous contacts. In this way, the recommender system not only learns user taste, but makes these learned preferences transparent and editable. As more and more relevant recommendations by anonymous contacts are made, the recommender network evolves and builds trust between reliable contacts that share common interests.