An clustering and optimized collaborative filtering recommendation based on interest measure
Sun Duo · Journal of Anhui University · 2007
Collaborative filtering is used extensively in personalized recommendation systems.With the magnitudes of users and commodities grow rapidly,resulting in the extreme sparsity of user rating data and the decreasing of real time performance.Traditional recommendation system work poor in this situation.This paper integrated collaborative filtering into interest measure to analyze the customer's personal taste,presents an clustering and optimized collaborative filtering recommendation based on interest measure.It can be proved that the new algorithm presented has a better performance corresponding to the known algorithm.The experiment shows that the approach is successful.