Social network and user context assisted personalization for recommender systems

In recommender systems, social networks are considered as a trusted source for user interests. In addition, user context can enhance users´ decision making. In this paper, we design a new architecture for user personalization which combines both social network data and context data. Our system aggregates a user´s preference data from various social networking services and then builds a centralized user profile which is accessible through public Web services. We also collect user´s contextual information and store it in a central space which is also accessible through public Web services. Based on Service Oriented Architecture, recommender systems can flexibly utilize users´ preference information and context to provide more desirable recommendations. We present how our system can integrate both types of data together and how they can be mapped in a meaningful way.