A collaborative filtering recommendation algorithm based on double neighbor choosing strategy

Collaborative filtering is the most successful and widely used recommendation technology in E-commerce recommender system. It can recommend products for users by collecting the preference information of similar users. However, the traditional collaborative filtering recommendation algorithms have the disadvantages of lower recommendation precision and weaker capability of attack-resistance. In order to solve the problems, a collaborative filtering recommendation algorithm based on double neighbor choosing strategy is proposed. Firstly, on the basis of the computational result of user similarity, the preference similar users of target user are chosen dynamically. Then the trust computing model is designed to measure the trust relation between users according to the ratings of similar users. The trustworthy neighbor set of target user is selected in accordance with the degree of trust between users. Finally, a novel collaborative filtering recommendation algorithm based on the double neighbor choosing strategy is designed to generate recommendation for the target user. Using the MovieLens and Netflix dataset, the performance of the novel algorithm is compared with that of others from both sides of recommendation precision and the capability of attack-resistance. Experimental results show that compared with the existing algorithms, the proposed algorithm not only improves the recommendation precision, but also resists the malicious users effectively.