Research On Feature Extraction And Ensemble Detection Approaches For Recommendation Attacks In Collaborative Filtering

Collaborative filtering recommender systems can filter out the information to satisfythe users’ interests according to the established user profiles and recommend theinformation to users actively. They can solve the information overload problem on theInternet effectively, which have been widely used in many fields, e.g., e-commerce sites.Due to their natural openness, however, attackers artificially inject a large number of fakeprofiles into a collaborative filtering recommender system in order to bias therecommendation results to their advantage. These "shilling" attacks or recommendationattacks bring great security risk to collaborative recommender systems. To reduce thesecurity risk produced by recommendation attacks, the detection approaches forrecommendation attacks have attracted widespread attention. On the basis ofcomprehensive analysis for the current research in this area, this paper has conductedfurther deep research on the feature extraction methods and detection approaches forrecommendation attacks.Firstly, aiming at the problem that the existing special feature extraction methods cannot describe the known recommendation attacks effectively, through introducingHilbert-Huang transform, term frequency-inverse document frequency, and mutualinformation a special feature extraction method for the known recommendation attacks isproposed. Based on the analysis of known recommendation attacks, Hilbert-Huangtransform, term frequency-inverse document frequency, and mutual information are usedto extract special features for these attacks. The extracted special features are used as thebasis of detecting known recommendation attacks.Then, aiming at the problem that the existing general feature extraction methods cannot describe the unknown recommendation attacks effectively, through introducingentropy a general feature extraction method for the unknown recommendation attacks isproposed. From the perspective of user rating distribution, entropy is used to extractgeneral features for the unknown recommendation attacks. The extracted general featuresare used as the basis of detecting unknown recommendation attacks. Next, aiming at the problem that the existing supervised detection approaches sufferfrom high false alarm ratio, an ensemble detection approach based on support vectormachine is proposed. The above proposed special features extraction method is used toextract features of user profiles. The bootstrap technique is used to generate the diversebase training sets. The generated base training sets are used to train support vectormachine to generate the base classifiers. These classifiers are used to detect the test sets.The majority voting strategy is used to integrate the detection results of the baseclassifiers.After that, aiming at the problem that the existing detection approaches can not detectthe unknown recommendation attacks effectively, an ensemble detection approach basedon bionic pattern recognition is proposed. The above proposed general features extractionmethod is used to extract features of user profiles. The technique of bionic patternrecognition is used to cover the samples of genuine profiles. User profiles outside thecoverage are judged as attack profiles. On this basis, through adjusting the area of thecoverage the base classifiers are generated for the detection of test data. The majorityvoting strategy is used to integrate the detection results of the base classifiers.Finally, the comparative experiments are conducted with the related work onMovieLens dataset. The effectiveness of the proposed approaches is verified.