Video Recommendation Method Based on Group User Behavior Analysis

This paper presents an effective solution for personalized video recommendation based on the weight increment and similar aggregation user behavior analysis algorithm. The method is implemented in three steps: first, the user behavior is analyzed using the RFM (Recentness, Frequency, Monetary amount) model, users with the same behavior are classified as a group; second, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users, and by using the VSM model for similarity calculation, the user similarity aggregation is realized; finally, the whole process of personalized video recommendation is completed by means of collaborative filtering. The proposed method can automatically collects user behavioral data and avoids direct video big data processing. In addition, the video recommend dynamically changes with the change of user behavior. The experiment results show that, the presented effective and stable, and the method achieves significantly increasement in precision and recall comparing with the single recommendation method.