Search and recommendation systems must effectively model user interests in order to provide personalized results. The proliferation of social software makes social network an increasingly important source for user interest modeling, because of the social influence and correlation among friends. However, there are large variations in people's contribution of social content. Therefore, it is impractical to accurately model interests for all users. As a result, applications need to decide whether to utilize a user interest model based on its accuracy. To address this challenge, we present a study on the accuracy of user interests inferred from three types of social content: social bookmarking, file sharing, and electronic communication, in an organizational social network within a large-scale enterprise. First, we demonstrate that combining different types of social content to infer user interests outperforms methods that use only one type of social content. Second, we present a technique to predict the inference accuracy based on easily observed network characteristics, including user activeness, network in-degree, out-degree, and betweenness centrality.
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