Personalized Recommendation Model Based on Dynamic Time

In the recommendation systems, it often exists some problems of non-real-time data, sparse data and cold start. First, the paper uses forgetting curve to track the changed of user’s interests with time, and then puts forward an improved K-Means clustering algorithm to cluster users, and finally uses the improved personalized recommendation algorithm. A model of personalized recommendation based on dynamic time is built. The experimental results show that the proposed personalized recommendation model can get more accurate recommendation performance and alleviate the cold start problem. It also can reduce the computing time and space complexity, and improve the quality of the recommendation of personalized recommendation algorithm.