Workshop on recommender systems and the social web

The exponential growth of the Social Web both poses challenges, and presents opportunities for Recommender System research. The Social Web has turned information consumers into active contributors who generate large volumes of rapidly changing online data. Recommender Systems strive to identify relevant content for users at the right time and in the right context but achieving this goal has become more difficult, in part due to the volume and nature of information contributed through the Social Web. The emergence of the Social Web marked a change in Web users' attitude to online privacy and sharing. Social media systems encourage users to implicitly and explicitly provide large volumes of information which previously they would have been reluctant to share. This information includes personal details such as location, age, and interests, friendship networks, bookmarks and tags, opinion and preferences which can be captured explicitly or more often by monitoring user interaction with the systems (e.g. commenting, friending, rating,tagging etc). These new sources of knowledge can be leveraged by Recommender Systems to improve existing techniques and develop new strategies which focus on social recommendation. In turn recommender technologies can play a huge part in fuelling the success of the Social Web phenomenon by reducing the information overload problem facing social media users. The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics. Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process. The topics of the submitted papers included, among others, the following main areas: Case studies and novel fielded social recommender applications Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc. Recommender system mash-ups, Web 2.0 user interfaces, rich media recommender systems Recommender applications involving users and groups directly in the recommendation process Exploiting folksonomies, social network information, interaction user context and communities or groups for recommendations Trust and reputation aware social recommendations Semantic Web recommender systems, use of ontologies and microformats Empirical evaluation of social recommender techniques, success and failure measures Social recommender systems in the enterprise The list of short papers, the workshop schedule and downloadable versions of the papers can be found at the workshop's homepage at: http://www.dcs.warwick.ac.uk/~ssanand/RSWEb.htm and are also published at: http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/