Personalized information filtering extracts the information specifically relevant to a user, based on the opinions of users who think alike or the content of the items that a specific user prefers. In this talk, we discuss latent models to utilize additional user-specific and content-specific predictors, for personalized prediction. In particular, we factorize a user-over-item preference matrix into a product of two matrices, each having the same rank as the original matrix. On this basis, we seek a sparsest latent factorization from a class of overcomplete factorizations, possibly with a high percentage of missing values. A likelihood approach is discussed, with an emphasis towards scalable computation. Examples will be given to contrast with popular methods for collaborative filtering and contented- based filtering. This work is joint with Y. Zhu and C. Ye.
More information on Xiaotong Shen may be found at http://users.stat.umn.edu/~xshen/