Know the Right People? Recommender Systems for Web 2.0

Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. However, the rapid increase in size of online communities and the availability of large amounts of shared data make discovering relevant content and finding related users a difficult task. Web 2.0 applications provide a rich set of structures and annotations that can be mined for a variety of purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Based on this model we propose recommendation mechanisms using methods from social network analysis, collaborative filtering, and machine learning. Our objective is to construct collaborative recommender systems that predict the utility of items, users or groups based on the multi-dimensional social environment of a given user.