Inferring User Interest Using Familiarity and Topic Similarity with Social Neighbors in Facebook

Uncovering user interest plays an important role to develop personalized systems in various fields including the Web and pervasive computing. In particular, online social networks (OSNs) are being spotlighted as the means to understand users´ social behavior out of abundant online social information. In this paper, we explore a computational method of inferring user interest in Facebook by combining the degree of familiarity and topic similarity with social neighbors based on social correlation phenomenon. By conducting a question-naire survey, we demonstrate that our proposed method increases the accuracy of inference by 12.4% compared to existing methods which do not consider the latent topic structure implied in social contents.