Relevance-based language modelling for recommender systems
A new recommendation approach based on the Relevance Modelling (RM) of the problem is proposed. The neighbour selection problem is improved by Posterior Probabilistic Clustering (PPC). Background information such as item popularity is successfully integrated by using RM models. Performance of the recommendation improves when more clusters are considered in the PPC technique. Combination of both contributions leads to an even better performance than their separate application.
recommender systems relevance models probabilistic clustering collaborative filtering