This item is a Paper in the Data Mining track.
We present a probabilistic model for generating personalised
recommendations of items to users of a web service. The
Matchbox system makes use of content information in the
form of user and item meta data in combination with col-
laborative filtering information from previous user behavior
in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped
into a low-dimensional ‘trait space’ in which similarity is
measured in terms of inner products. The model can be
trained from different types of feedback in order to learn
user-item preferences. Here we present three alternatives:
direct observation of an absolute rating each user gives to
some items, observation of a binary preference (like/ don’t
like) and observation of a set of ordinal ratings on a user-
specific scale. Efficient inference is achieved by approxi-
mate message passing involving a combination of Expecta-
tion Propagation (EP) and Variational Message Passing. We
also include a dynamics model which allows an item’s popu-
larity, a user’s taste or a user’s personal rating scale to drift
over time. By using Assumed-Density Filtering (ADF) for
training, the model requires only a single pass through the
training data. This is an on-line learning algorithm capable
of incrementally taking account of new data so the system
can immediately reflect the latest user preferences. We eval-
uate the performance of the algorithm on the MovieLens and
Netflix data sets consisting of approximately 1,000,000 and
100,000,000 ratings respectively. This demonstrates that
training the model using the on-line ADF approach yields
state-of-the-art performance with the option of improving
performance further if computational resources are available
by performing multiple EP passes over the training data.
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