Research on Personalized Recommendation Algorithm Based on Collaborative Filtering and Partition Clustering
Collaborative filtering is one of the most widely used and successful recommendation technologies in e-commerce recommendation system. However, the traditional collaborative filtering recommendation algorithm is confronted with the problem of data sparseness in the face of geometric multiplication of agricultural products, leading to reduced efficiency and accuracy. To solve this problem, this paper proposes a collaborative filtering recommendation algorithm based on partition clustering. In the traditional collaborative filtering algorithm, the idea of user interest matrix clustering. Firstly use the known user clustering; Secondly, the degree of the centralized user and each cluster center is obtained by the user interest matrix; then get the similarity between the target user and the known user; Eventually get recommended. The experimental results show that the improved algorithm makes the spatial search ratio of the target user's nearest neighbor search greatly reduced, and improve the efficiency and accuracy of the recommendation to a certain extent.