Probabilistic relevance ranking for collaborative filtering |
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| Authors: | Jun Wang Stephen Robertson Arjen P de Vries Marcel J T Reinders |
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| Institution: | (1) University College London, Adastral Park Campus, Ross Building 2, Martlesham Heath, Ipswich, IP5 3RE, UK;(2) Microsoft Research, Cambridge, UK;(3) CWI, Amsterdam, The Netherlands;(4) Delft University of Technology, Delft, The Netherlands |
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| Abstract: | Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are
specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice
we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N
list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly
cast as a relevance ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic
item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin,
different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions
to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the
Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore,
we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance),
which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets
demonstrate that the proposed methods perform significantly better than other strong baselines.
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| Keywords: | Collaborative filtering Recommender systems Probability Ranking Principle Relevance ranking Personalization |
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