Evaluation of Collaborative Filtering Based Recommender Systems against Segment-Based Shilling Attacks

Collaborative 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.