User modeling is essential for any information service system (e.g., search engines, recommender systems, and computational advertising) to optimize its service to the end users. The level of user understanding directly determines the upper bound of optimality that such a system can achieve when assisting its users. Unfortunately, due to the limited support in current human-computer interaction interfaces, users are restricted to express their complex information needs via simple keyword queries or some predefined categories, which are too shallow to capture users' higher-level latent intents that influence their decisions and preferences. As a result, there is great demand to build effective computational models to analyze users' generated data and their behavior patterns when they interact with such systems, and understand users' underlying intents so as to enable the systems to provide optimal and personalized services for each individual user.This dissertation aims at developing general and effective computational methods for user modeling based on two specific types of user-generated data. First, a novel opinionated text mining problem called Latent Aspect Rating Analysis (LARA) is proposed and studied. Clearly distinct from all previous works in opinion analysis that mostly focus on integrated entity-level opinions, LARA for the first time reveals individual users' latent sentiment preference at the level of topical aspects in an unsupervised manner. A prototype system called ReviewMiner has been developed based on the techniques proposed in the LARA work. Second, users' interaction patterns recorded in search engine logs (e.g., their issued queries and clicked documents) are explored for understanding their longitudinal information seeking behaviors. Various important problems related to users' search behaviors have been addressed, including long-term search task identification, personalized ranking model adaptation prediction and task-level search satisfaction.
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