Recommender Systems and the Social Web

Workshop proceedings online at CEUR-WS

The exponential growth of the Social Web poses both challenges and new opportunities for recommender systems research. The Social Web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, 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. Users of social media on the Web often explicitly provide personal information or implicitly express preferences through their interactions with other users or with resources (e.g. tagging, friending, rating, commenting, etc.).

This Social Web therefore 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 bring together researcher and practitioners to explore, discuss, and understand challenges and new opportunities for recommender systems and the Social Web. We solicit original contributions in the following areas:

• Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.
• Leveraging Models user behavior on the Social Web for recommendation
• Recommender systems mash-ups, intelligent user interfaces, rich media recommender systems
• Collaborative knowledge authoring, collective intelligence
• Topic emergence and evolution on the Social Web and their role in recommendation process
• Recommender applications involving users or groups directly in the recommendation process
• Exploiting folksonomies, social network information, user interactions, and communities recommendation process
• The role of context in Social Web recommendation
• 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 novel fielded social recommender applications
• Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation
• Social recommender systems in the enterprise
• Recommendation for groups