Statistical and probabilistic models are concerned with the use of observed sample results to make statements about unknown, dependent parameters. In user modeling, these parameters represent aspects of a user’s behaviour, such as his or her goals, preferences, and forthcoming actions or locations. Recent technological advances, in particular increased computational power, together with anytime, anyplace access to computers, and the information explosion associatedwith the Internet, provide new opportunities for information dissemination and information gathering. On one hand, people have access to large repositories of information in digital form. On the other hand, information providers can find out more about their users’ requirements by logging people’s activities. This mixture of vast electronic content and increased knowledge about people’s actions provides an opportunity to harness statistical and probabilistic models to build user models that support the delivery of personalized content. This usage of statistical and probabilistic models has been manifested in UMUAI for the last ten years. Particularly noteworthy are the articles in the Special Issue on Machine Learning for User Modeling (1998); the survey articles by Zukerman and Albrecht and byWebb et al. in the 10-year anniversary issue, respectively on predictive statistical models for user modeling (Zukerman and Albrecht 2001), and on machine learning for user modeling (Webb et al. 2001); Burke’s survey on recommender systems (Burke 2002); and Pierrakos et al.’s survey on Web usage mining (Pierrakos et al. 2003). These articles identified several challenges that user modeling presents to statistical and probabilistic modeling techniques. We classify these challenges into three categories: (1) limitations of current user modeling approaches, (2) dynamic nature of user modeling data, and (3) efficiency considerations.