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We describe CFW, a computationally efficient algorithm for collaborativefiltering that uses posteriors over weights of evidence. In experiments on realdata, we show that this method predicts as well or better than other methods insituations where the size of the user query is small. The new approach worksparticularly well when the user s query CONTAINS low frequency(unpopular)items.The approach complements that OF dependency networks which perform wellWHEN the size OF the query IS large.Also IN this paper, we argue that the USEOF posteriors OVER weights OF evidence IS a natural way TO recommend similaritems collaborative - filtering task.

CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence