User-based Trust Model For Collaborative Recommendation Attack Defense

With the expansion of the scope of application system, in the business, some goods are recommended for ascending frequency, commercial competitors began frequently in system with false record, trying to score goals for influence of commodities, distorted recommended recommend frequency accuracy and integrity, thus obtaining business profits. Due to the cooperative system has natural open, the attacker in mastering a system under the premise of information, can be successful in collaborative recommended recommend the system. This behavior affects the accuracy and integrity of the system, thus caused.This paper expounds the basic principles and main collaborative recommended cooperative filter algorithm recommendation algorithm, based on this, the paper introduces the current research collaborative filtering attacks against model, and focuses on the sexual assaults, ascension partition.formal definition model, and the model and structure of collaborative recommend against model algorithm. Is proposed based on users trust model, through the user attack on the system of feedback, recommended projects with the neighbors to users TuiJianFen analysis evaluation, introducing the trust mechanism, to reduce the system to recommend against users. In the experiment, we use the average increment to evaluate the new model based on trust to ascend the average attack and the detection performance of random attack, a large number of experiments shows that this model can effectively reduce the random collaborative-filtering recommend against and average attack produces effect, thus greatly improves the reliability of the system.