Collaborative Filtering Recommendation Model Based on User´s Credibility Clustering

Aiming at the long response time, inaccurate recommendation and cold-start problems that faced by present recommendation algorithm, this paper, taking movie recommendation system as an example, proposes a collaborative filtering recommendation model based on user´s credibility clustering. This model divides recommendation process into offline and online phases. Offline, it uses the result of user´s credibility for clustering and then writes the clustered information into a table in database. Online, finds the cluster that target user belongs to and then gives recommendation. As a whole, the model reduces the response time, improves the accuracy of the recommendation rate, and solves the new user´s cold-start problem.