An Optimized Collaborative Filtering Recommendation Algorithm

Collaborative filtering is used extensively in personalized recommendation systems With the development of E commerce, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparsity of user rating data Traditional similarity measure methods work poor in this situation Considering the extreme sparsity of user rating data, collaborative filtering algorithm based on item rating prediction is introduced, then the item similarity is computed by using a new revised conditional probability expression, the quality of the recommended result can be effectively improved Finally an optimized collaborative filtering recommendation algorithm is presented 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