Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
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In memory-based collaborative filtering, the existing methods conduct a prediction based on the overall consistency of two users or items. The major problem with these methods is that it is hard to find users/items that are overall consistent with the test user/item in the system. In addition, these methods are sometimes being over optimistic, and disregard some useful information in user profiles in making a prediction. This paper exposes the drawbacks in these methods and proposes an inference-based recommendation scheme to overcome those drawbacks. This model is based on the fact that any two users may have common interest genres as well as different ones, with the capability of making full use of rating information to capture accurately the relevance between item and user. Experimental results from two popular public datasets, EachMovie and MovieLens, show that our approach improves significantly the prediction accuracy.