Collaborative filtering algorithm based on optimized clustering and fusion of user attribute features

Aiming at the problems of low recommendation quality, low recommendation efficiency, and cold startup in the collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on optimized K-means clustering algorithm and user attribute features is proposed. According to the user attribute information, the optimized K-means clustering algorithm is used to cluster them; Multiple clusters are generated, and a novel similarity calculation model is formed by combining user attribute features in each cluster; Considering that users' interests will change dynamically with time, time factor is introduced into traditional scoring similarity; Through this model, the nearest neighbor of the target user is found, and the recommendation list is generated to realize the recommendation. The experimental results produced on the MovieLens datasets show that this algorithm can shorten the algorithm operation time and solve the cold start problem while improving the recommendation efficiency and recommendation accuracy.