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Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that

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The development of information and communication technologies offers the possibility of collecting and sharing customer views, comments and ratings about products and services over the Internet. Customers generally make these evaluations based on multiple criteria. This study uses such data recorded on Skytrax to analyse the performance of leading airlines. It does so using the a multicriteria decision making technique (Promethee II), and the criteria weight values required for the Promethee II method are obtained from a Multi-Layer Perceptron (MLP), an artificial neural network method. According to the results obtained, ANA airline has shown improvements in the years and moved up to the top, while the ranking of United airline within two years has not changed. The paper provides details of the technique and graphically presents results to highlight where airlines possess advantages over their competitors.