Hybrid Recommendation System Based on Affinity Propagation Clustering

Collaborative filtering(CF) is one of the most valuable technologies of recommendation system. It can effectively mine users' potential hobbies and make reasonable recommendations to users. But the technology applied in the actual recommendation system is still constrained by cold start, data sparsity, scalability and so on. In this paper, a hybrid collaborative filtering recommendation model based on affinity propagation clustering(AP) was proposed for cold start and data sparseness. The model first clustered based on the tag attributes of items, and mined items of the same type and calculated the degrees of association between similar items. Then, the historical interaction data was used to calculate the similarities of all items in the model. Finally, a certain proportion was mixed to form an item similarity matrix which was used to recommendation for users. Compared with the traditional collaborative filtering recommendation model, it not only greatly improves the recommendation precision, but also improves the recall rate of the item and provides a better recommendation experience for the user.