_:n6921723ffdd64b259fcfb202929b506axb75
<http://schema.org/description>
"In recent years, recommender system has become an indispensable function in\nall e-commerce platforms. The review rating data for a recommender system\ntypically comes from open platforms, which may attract a group of malicious\nusers to deliberately insert fake feedback in an attempt to bias the\nrecommender system to their favour. The presence of such attacks may violate\nmodeling assumptions that high-quality data is always available and these data\ntruly reflect users' interests and preferences. Therefore, it is of great\npractical significance to construct a robust recommender system that is able to\ngenerate stable recommendations even in the presence of shilling attacks. In\nthis paper, we propose GraphRfi - a GCN-based user representation learning\nframework to perform robust recommendation and fraudster detection in a unified\nway. In its end-to-end learning process, the probability of a user being\nidentified as a fraudster in the fraudster detection component automatically\ndetermines the contribution of this user's rating data in the recommendation\ncomponent; while the prediction error outputted in the recommendation component\nacts as an important feature in the fraudster detection component. Thus, these\ntwo components can mutually enhance each other. Extensive experiments have been\nconducted and the experimental results show the superiority of our GraphRfi in\nthe two tasks - robust rating prediction and fraudster detection. Furthermore,\nthe proposed GraphRfi is validated to be more robust to the various types of\nshilling attacks over the state-of-the-art recommender systems."
<https://deepai.org/profile/lizhen-cui>