Extraction of Hidden Common Interests between People Using New Social-Graph Representation

It can be essential in the new-generation content services to predict the potential demands of people, which they themselves have not recognized or cannot express precisely. Social graphs representing the relationships between people are used for predicting demand in current Internet-based services. However, these graphs cannot represent the relationships of two users residing in common communities or common places. We propose representing not only a person but also things like communities and social events together as a single node in a social graph. This representation allows us to estimate who shares potential interests with a given person. We evaluated the estimation accuracy of our representation using an actual relational dataset from an academic database. Results show that our representation can estimate if two people share common interests that cannot be found with conventional methods that only use human nodes for estimation, and it can estimate the relations without using human nodes.