RESSON 2013 - Workshop on Recommender Systems for Social Networks (RESSON)

Recommenders are a flagship application for knowledge discovery and data mining since they have become an important tool for dealing with and overcoming the information overload in massive and complex data domains. At the same time, social networking in the Web 2.0 involves gigabytes of data that can be mined to make recommendations while they allow for developing new insights into people's behavior; such insights can be used for improving the quality of the recommendations. However, the exploitation of social information in recommender systems brings new challenges to the area of data mining and knowledge discovery.
The goal of this workshop is to highlight these challenges and to solicit papers that propose state-of-the-art solutions to recommender tasks on the basis of social information. Our workshop shall provide a framework for directing and inspiring new pathways of data mining and knowledge discovery research within recommender systems.
Open questions to be addressed in the solicited papers include:
What is the effect of one's social network in their recommendation?
What are the best strategies for recommending team formation?
How to integrate heterogeneous data and different social network information?
How to ensure privacy whilst still providing relevant recommendations to users?
How should the few advances on online recommenders be transferred to the context of social networks?
How should a recommender respond to drifts and shifts in the behavior of people, how should advances on learning under drift be transferred in this context?
Topics
We encourage papers submitted to RESSON to focus on the following topics:
Dynamics of social recommenders
Online recommenders
Exploiting bursts in recommenders
Capturing time in social recommenders
Link recommendation and prediction in social networks
Recommendation in time evolving social networks
Mining and learning from social data
Opinion mining for recommendations
Latent models and beyond for social recommenders
Group recommendation in social networks
Collaborative filtering in social networks
Recommendation in social media
Team formation and team recommendation
Community recommendations in social networks
Online dating recommendations
Expert search and expertise recommendation
Rating prediction in social rating networks
Mining social influence
Learning for tagommenders
Learning to recommend images from social data
Scalability of recommender systems
Learning robust models
Fast online recommenders
Indexing social data for recommenders
Learning recommenders from complex data
Location-aware recommenders
Multi-criteria recommenders
Recommendations for the long tail
Incremental learning for social recommendations
Cross-domain recommendation in social rating networks
Privacy issues in recommender systems
Privacy of recommendation in social networks
Recommendation in social networks with distrust