  Most existing content-based filtering approaches learn user profiles
independently without capturing the similarity among users. Bayesian
hierarchical models \cite{Zhang:Efficient} learn user profiles jointly and have
the advantage of being able to borrow discriminative information from other
users through a Bayesian prior. However, the standard Bayesian hierarchical
models assume all user profiles are generated from the same prior. Considering
the diversity of user interests, this assumption could be improved by
introducing more flexibility. Besides, most existing content-based filtering
approaches implicitly assume that each user profile corresponds to exactly one
user interest and fail to capture a user's multiple interests (information
needs).
  In this paper, we present a flexible Bayesian hierarchical modeling approach
to model both commonality and diversity among users as well as individual
users' multiple interests. We propose two models each with different
assumptions, and the proposed models are called Discriminative Factored Prior
Models (DFPM). In our models, each user profile is modeled as a discriminative
classifier with a factored model as its prior, and different factors contribute
in different levels to each user profile. Compared with existing content-based
filtering models, DFPM are interesting because they can 1) borrow
discriminative criteria of other users while learning a particular user profile
through the factored prior; 2) trade off well between diversity and commonality
among users; and 3) handle the challenging classification situation where each
class contains multiple concepts. The experimental results on a dataset
collected from real users on digg.com show that our models significantly
outperform the baseline models of L-2 regularized logistic regression and
traditional Bayesian hierarchical model with logistic regression.
