Using social ties in group recommendation

Abstract

The social web is a mass of activity, petabytes of data are generated yearly. The social web has proven to be a great resource for new recommender system techniques and ideas. However it would appear that typically these techniques are not so social, as they only generate recommendations for a user acting alone. In this paper we take the social graph data and preference content (via Facebook) of 94 user study participants and generate social group recommendations for them and their friends. We evaluate how different aggregation policies perform in deciding the final group recommendation. Our findings show that in an offline evaluation an aggregation policy which takes into consideration social weighting outperforms other aggregation policies.