A Shilling Group Detection Framework Based on Deep Learning Techniques

Research studies have shown that online recommender systems are under the threat of group shilling attacks, in which attackers attempt to distort the recommendation results of particular items by cooperatively injecting fake profiles. Existing detection methods usually divide candidate groups at first and then use the hand-crafted features to recognize shilling groups. However, the detection performance of existing methods depends highly on the quality of candidate groups obtained. Moreover, extracting features manually is time-consuming. To overcome these limitations, we propose a shilling group detection framework based on the sparse autoencoder and modified GraphSAGE model. First, we use the sparse autoencoder to obtain rating features of users from the rating dataset. Second, we analyse user collusive degrees to calculate user transition probabilities. Third, we build a user relation graph and utilize the modified GraphSAGE model to perform user classification. Finally, shilling groups are gathered according to the neighbour relations. Extensive experiment demonstrates that the proposed framework performs better on different datasets than baseline methods.