Socially-aware Video Recommendation using Users' Profiles and Crowdsourced Annotations

The recent explosive growth in the use of social networks has raised the question of how to meet the emerging demand for services that address the interests of the users. In this paper we show how considering homophily in social networks can improve video recommendation, using inferred user profiles and modeling users' interests. We propose a socially-aware framework for video commenting, sharing and interest discovery that combines recommendation algorithms, clustering techniques, tools for video tagging and evaluation of tag semantic relatedness. The system allows to connect to friends, curate a personal profile and get video recommendations through a social network.