Abstract:

In order to improve the accuracy of collaborative filtering, the paper proposes a new collaborative filtering based on the dual-threshold neighbors finding method in the perspective of how to find the truly relevant user/item group. This method can take full advantage of existing sparse user-rate matrix to find some users or items which are strong relative to the active user/item, and they can participate in the progress of calculating predicate rate. The experimental results show that the recommendation accuracy of the new algorithm is better than traditional collaborative filtering and some improved algorithms.

Key words: collaborative filtering, sparse matrix, personalized recommendation, dual-threshold