- Recommender systems are important to help users select
relevant and personalised information over massive
amounts of data available. We propose an unified framework
called Preference Network (PN) that jointly models
various types of domain knowledge for the task of recommendation.
The PN is a probabilistic model that systematically
combines both content-based filtering and collaborative
filtering into a single conditional Markov random
field. Once estimated, it serves as a probabilistic database
that supports various useful queries such as rating prediction
and top-N recommendation. To handle the challenging
problem of learning large networks of users and
items, we employ a simple but effective pseudo-likelihood
with regularisation. Experiments on the movie rating data
demonstrate the merits of the PN.
(from crpit.com)
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