Statistical learning approaches to information filtering

Enabling computer systems to understand human thinking or behaviors has ever been an exciting challenge to computer scientists. In recent years one such a topic, information filtering, emerges to help users find desired information items (e.g. movies, books, news) from large amount of available data, and has become crucial in many applications, like product recommendation, image retrieval, spam email filtering, news filtering, and web navigation etc.. An information filtering system must be able to understand users’ information needs. Existing approaches either infer a user’s profile by exploring his/her connections to other users, i.e. collaborative filtering (CF), or analyzing the content descriptions of liked or disliked examples annotated by the user, i.e. content-based filtering (CBF). Those methods work well to some extent, but are facing difficulties due to lack of insights into the problem. This thesis intensively studies a wide scope of information filtering technologies. Novel and principled machine learning methods are proposed to model users’ information needs. The work demonstrates that the uncertainty of user profiles and the connections between them can be effectively modelled by using probability theory and Bayes rule. As one major contribution of this thesis, the work clarifies the “structure” of information filtering and gives rise to principled solutions. In summary, the work of this thesis mainly covers the following three aspects: • Collaborative filtering : We develop a probabilistic model for memorybased collaborative filtering (PMCF), which has clear links with classical memory-based CF. Various heuristics to improve memory-based CF have been proposed in the literature. In contrast, extensions based on PMCF can be made in a principled probabilistic way. With PMCF, we