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Prediction and recommendation in online media

With billions of internet users, online media services have become commonplace. Prediction and recommendation for online media are fundamental problems in various applications, including recommender systems and information retrieval. As an example, accurately predicting user behaviors improves user experiences through more intelligent user interfaces. On the other hand, user behavior prediction in online media is also strongly related to behavior targeting and online advertisement which is the major business for most consumer internet services. Estimating and understanding users' click behaviors is a critical problem in online advertising. In this dissertation, we investigate the prediction and recommendation problems in various online media. We find a number of challenges: high order relations, temporal dynamics, complexity of network structure, high data sparsity and coupled social media activities. We consider user behavior understanding and prediction in four areas: tag prediction in a social tagging system, link prediction in microblogging services, multi-context modeling in online social media and click prediction in sponsored search. In such topics, based on real world data, we analyze user behaviors and discover patterns, properties and challenges. Subsequently, we design specific models for online user behavior prediction in various online media: a probabilistic model for personalized tag prediction, a user-tag-specific temporal interests model for tracking users' interests over time in tagging systems, a personalized structure based link prediction model for micro-blogging systems, a generalized latent factor model and Bayesian treatment for modeling across multiple contexts in online social media, a context-aware click model and framework for estimating ad group performance in sponsored search. Our extensive experiments on large-scale real-world datasets show our novel models advance the state-of-the-art.