Trustworthy and profit: A new value-based neighbor selection method in recommender systems under shilling attacks

Abstract

User-based collaborative filtering recommender systems are widely deployed by e-retailers to facilitate customer’ decision-making and enhance e-retailers' profitability. Despite the advantages these systems provide, their recommendation effectiveness is vulnerable to attacks from malicious users who inject biased ratings. Such attacks against recommender systems are called shilling attacks. Although several shilling attack detection mechanisms have been proposed in previous studies, their detection performance is limited in various attack conditions. Furthermore, few of these mechanisms consider the value-dimension associated with recommendations, which is crucial for e-retailers. This research proposes a novel approach called Value-based Neighbor Selection (VNS) to address the above limitations. The objective of this approach is to protect recommender systems from shilling attacks while improving e-retailers' profitability. It alleviates the aforementioned problems through strategically selecting neighbors whose preferences are then used to make recommendations. We have performed a series of empirical validations in various attack conditions to compare the performance of the proposed method and three benchmark methods, in terms of both recommendation accuracy and e-retailer profitability. The results show the advantages of the proposed method in balancing customer satisfaction and e-retailer profitability.