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Combining Collaborative Filtering with Meta Data for Scalable Recommendations

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I will present a probabilistic model for generating personalised recommendations of items to users of a web service. The system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behaviour 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 I 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 approximate message passing involving a combination of Expectation Propagation (EP) and Variational Message Passing. I also include a dynamics model which allows an items popularity, a user’s taste or a user’s personal rating scale to drift over time.

This talk is part of the Inference Group series.

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