Shilling recommender systems for fun and profit

By Shyong K. Lam, John Riedl. 2004

755 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Marketing

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Citations in the corpus

Stable and Fair Classification (2019, background): “The dataset, that incorporates collected personal information, may be noisy due to measurement errors, privacy issues, or even data poisoning attacks [46, 56, 59, 6] and, hence, it is desirable that the fair classiﬁer also be stable against perturbations in the dataset.”
Preventing shilling attacks in online recommender systems (2005, background): “…helping users .nd Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.”
Stability of matrix factorization for collaborative filtering (2012, background): “Targeted Attacks include average attack (Lam & Riedl, 2004), segment attack and bandwagon attack (Mobasher et al., 2007).”
Denoising Implicit Feedback for Recommendation (2020, background): “Prior work [17, 29] has tried to evaluate the robustness of recommender systems under various attack methods, such as shilling attacks [17] and fuzzing attacks [29].”
Trust in recommender systems (2005, background): “There are many motivations for users attempting to mislead recommender systems, including profit, and malice, as outlined in [8].”
Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems (2005, methodology): “[5] show an attack model in which profiles consist of random values (except of”
TrustGuard: countering vulnerabilities in reputation management for decentralized overlay networks (2005, neutral): “Our experiments show that, comparing with existing reputation systems, our framework is highly dependable and e.ective in countering malicious nodes regarding strategic oscillating behavior, .ooding malevolent feedbacks with fake transactions, and dishonest feedbacks.”
On the stability of recommendation algorithms (2010, background): “In particular, since recommender systems depend heavily on input from users, they are subject to manipulations and attacks [13-17].”
Recommender systems: from algorithms to user experience (2012, background): “However, in a large recommender community, an effective attack on popular items is difficult because of the volume of countervailing data (Lam and Riedl 2004).”
Evaluating Recommendation Systems (2011, background): “empirically measuring average cost of a successful attack [36, 10].”
Comparison of collaborative filtering algorithms (2011, background): “— Shilling [Lam and Riedl 2004; Chirita et al. 2005].”
Recommender Systems (2016, neutral): “In general, significant profit-driven motivations exist for some users to enter fake ratings [158, 329, 393, 444].”
Shilling attacks against recommender systems: a comprehensive survey (2012, background): “This type of attack is alternatively called RandomBot attack (Chirita et al. 2005; Lam and Riedl 2004).”
Collaborative Filtering Recommender Systems (2011, background): “Lam and Riedl [82] found that profile-based attacks can influence predictions and recommendation, but that item–item CF is less vulnerable than user–user to such attacks.”
Privacy-preserving matrix factorization (2013, background): “Privacy in recommender systems has been studied under several contexts, including the use of trusted hardware [1] as well as the susceptibility of a system to shilling attacks ( i.e. , the injection of false ratings to manipulate the recommendation outcome) [38, 47].”