Using social data as context for making recommendations

Web-based knowledge systems support an impressive and growing amount of information. Among the difficulties faced by these systems is the problem of overwhelming the user with a vast amount of data, often referred to as information overload. The problem has escalated with the ever increasing issues of time constraints and the extensive use of handheld devices. The use of context is one possible way out helping with this situation. To provide a more robust approach to context gathering we propose the use of Social Web technologies alongside the Semantic Web. As the social web is heavily used it could provide a better understanding of a user's interests and intentions. The proposed system gathers information about users from their social web identities and enriches it with ontological knowledge. Thus an interest model for the user can be created which can serve as a good source of contextual knowledge. This work bridges the gap between the user and system searches by analyzing the virtual existence of a user and making interesting recommendations accordingly.