Research On Recommendation Attack Detection Algorithm For Collaborative Filtering Recommender Systems

As one of the most successful recommendation system, collaborative filteringrecommendation system has a wide range of applications in e-commerce. Sincecollaborative recommender systems must be open to user input, it is difficult to design asystem that cannot be attacked. Some malicious users can provide a number of userprofiles to the system for some commerce purposes, trying to influence the system’sbehavior. This can make a serious impact on the recommendation quality of the system.So, how to solve the security problem of recommended system is urgently needed. On thebasis of the research and analysis about the present situation at home and abroad, thispaper has further conducted deep research of the security problem of recommendedsystem.Firstly, the existing supervised approaches suffer from the problem that detectionmodel cannot update incrementally with the increase of user profiles. Aiming at thisproblem, an algorithm for detecting recommendation attack based on incrementallearning is proposed. An algorithm for building a training set is proposed to choose thebest label samples used to build a classifier. Then newly labeled samples are used to trainthe classifier incrementally in order to make it more reasonable to cover attack profiles.Finally, the attack detection method based on statistical features is proposed todistinguish attack profiles from genuine user profiles.Secondly, according to the problem that the existing attack detection algorithmshave a high misjudgment rate for genuine user profiles, an algorithm for detectingrecommendation attack based on multiple risk factors is proposed, in which multiple riskfactors, including time suspicious degree, risk feedback, punish function, and activedegree, are incorporated to reflect uncertainty in various angles. Then the weight ofclassification is set up by information entropy theory for these risk factors, whichovercomes the shortage of traditional method, in which the weight is set up by subjectivemanners. Finally, the attack detection method distinguishes attack profiles from genuineuser profiles based on behaviors risk evaluation. Finally, we give the experimental evaluations and analysis of the algorithmsproposed in this paper, compare the performance between the proposed algorithms andother existing algorithms, and make the conclusions and prospects for the further search.