Recommender Systems and the Social Web

The Social Web has been enjoying huge popularity in recent years, attracting millions of visitors on sites such as Facebook, Delicious, YouTube. We are no longer mere consumers of information, but we also actively participate in social networks, upload our personal photos, share our bookmarks, write web logs and annotate and comment on the information provided by others. Following the exponential growth in the popularity of Social Web sites, many traditional, non-social sites, are now implementing social features. Likewise many enterprises are deploying internal social media sites to support expertise location and sharing of work-related information and knowledge.

This Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.

• New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders can not only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development. There are also opportunities for novel recommender applications on the social Web that directly involve humans in the recommendation process, for example, users or groups making recommendations to other users, or online multi-user games leading to recommendations.
• Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. This social layer can also be used as evidence on which to infer relationships and trust levels between users for recommendation generation.
• The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people. Or, the design and development of more interactive and richer recommender system user interfaces that enable users to express their opinions and preferences in an intuitive and effortless manner.
• Recommender technology can assist social systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.

Topics

The goal of this workshop is to explore, discuss, and understand new opportunities for recommender systems and the social Web. We solicit original contributions in the following areas:

• Economy of community-based systems:
  Encouraging users to contribute and sustain participation.
• Social network and folksonomy development:
  Recommending friends, tags, bookmarks, blogs, music, communities etc.
• Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems
• Collaborative knowledge authoring, collective intelligence
• Recommender applications involving users or 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 or microformats
• Empirical evaluation of social recommender techniques, success and failure measures
• Case studies and fielded applications
• Barriers of acceptance for social recommendations and the role of social dynamics and online identities in acceptance of recommendations