WORKSHOP TOPICS
Social Recommender Systems (SRSs) aim to alleviate information overload over social media users by presenting the most attractive and relevant content, often using personalization techniques adapted for the specific user. Social media and recommender systems can mutually benefit from one another. On the one hand, social media introduces new types of public data and metadata, such as tags, comments, votes, and explicit people relationships, which can be utilized to enhance recommendations. On the other hand, recommender systems can significantly affect the success of social media, ensuring each user is presented with the most attractive and relevant content, on a personal basis.

This workshop aims at bringing together researchers and practitioners around the emerging topics of recommender systems within social media in order to: (1) share research and techniques used to develop effective social media recommenders, from algorithms, through user interfaces, to evaluation (2) identify next key challenges in the area, and (3) identify new cross-topic collaboration opportunities. To take advantage of the WWW setting and its broad and diverse audience, we are in particularly encouraging two research sub-topics of the area: 1) studying new emerging applications for recommender systems on the Social Web 2) using new sources of knowledge especially Big Data generated by people and machine to enhance current techniques and develop new methods for recommender systems on the Social Web.

Topics of interests include, but are not limited to:

Social recommender technologies and applications
Model of recommendation context for social recommender systems
Characteristics of online social sites in need of social recommenders
Culture-specific social recommenders
New algorithms suitable for social recommender systems
People recommendation and social matching
Filtering and personalization of social streams
Emerging applications for social recommender systems
Recommendations for groups and communities
Recommender Systems and the semantic web
Social recommender systems in the enterprise
Diversity and novelty in social recommender systems
Recommendations for new social media users
User Interfaces in social recommender systems
Transparency and explanations in SRS
Adaption and personalization for SRS
User feedback in SRS
Trust and reputation in SRS
Social awareness and visualization
Privacy of SRS
Evaluation
Evaluation methods and evaluations of SRS
User studies
Crowdsourcing for recommendation evaluation
Organizers
Ido Guy, IBM Haifa Research Lab, Israel
Li Chen, Hong Kong Baptist University, Hong Kong
Michelle X. Zhou, IBM Almaden Research Lab, USA