Who are Like-minded: Mining User Interest Similarity in Online Social Networks

In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China

Modeling and Analysis of Scholar Mobility on Scientific Landscape

Scientific literature till date can be thought of as a partially revealed landscape, where scholars continue to unveil hidden knowledge by exploring novel research topics. How do scholars explore the scientific landscape , i.e., choose research topics to work on? We propose an agent-based model of topic mobility behavior where scholars migrate across research topics on the space of science following different strategies, seeking different utilities. We use this model to study whether strategies widely used in current scientific community can provide a balance between individual scientific success and the efficiency and diversity of the whole academic society. Through extensive simulations, we provide insights into the roles of different strategies, such as choosing topics according to research potential or the popularity. Our model provides a conceptual framework and a computational approach to analyze scholars' behavior and its impact on scientific production. We also discuss how such an agent-based modeling approach can be integrated with big real-world scholarly data.Comment: To appear in BigScholar, WWW 201