Visible to the public Evaluation of Collaborative Filtering Based Recommender Systems against Segment-Based Shilling Attacks

TitleEvaluation of Collaborative Filtering Based Recommender Systems against Segment-Based Shilling Attacks
Publication TypeConference Paper
Year of Publication2019
AuthorsBansal, Saumya, Baliyan, Niyati
Conference Name2019 International Conference on Computing, Power and Communication Technologies (GUCON)
KeywordsAccuracy, binary collaborative filtering, binary rating, CF techniques, collaborative filtering, collaborative filtering based recommender systems, F-measure, human factors, hybrid approach, hybrid system, nuke attack, prediction shift, pubcrawl, push attack, recommender system, recommender systems, Resiliency, Scalability, security of data, segment-based shilling attacks, segment-focused attack, segmentation attack, stand-alone rating item-based recommendation, stand-alone rating user-based recommendation, Vulnerability
AbstractCollaborative filtering (CF) is a successful and hence most widely used technique for recommender systems. However, it is vulnerable to shilling attack due to its open nature, which results in generating biased or false recommendations for users. In literature, segment attack (push attack) has been widely studied and investigated while rare studies have been performed on nuke attack, to the best of our knowledge. Further, the robustness of binary collaborative filtering and hybrid approach has not been investigated against segment-focused attack. In this paper, from the perspective of robustness, binary collaborative filtering, hybrid approach, stand-alone rating user-based, and stand-alone rating item- based recommendation have been evaluated against segment attack on a large dataset (100K ratings) which is found to be more successful as it attacks target set of items. With an aim to find an approach which reflects a higher accuracy in recommending items and is less vulnerable to segment-based attack, the possibility of any relationship between accuracy and vulnerability of six CF approaches were studied. Such an approach needs to be re-examined by the researchers marking the future of recommender system (RS). Experimental results show negligible positive correlation between accuracy and vulnerability of techniques. Publicly available dataset namely MovieLens was used for conducting experiments. Robustness and accuracy of CF techniques were calculated using prediction shift and F-measure, respectively.
Citation Keybansal_evaluation_2019