350 research outputs found
Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering
This paper examines the effect of Recommender Systems in security oriented issues. Currently research has begun to evaluate the vulnerabilities and robustness of various collaborative recommender techniques in the face of profile injection and shilling attacks. Standard collaborative filtering algorithms are vulnerable to attacks. The robustness of recommender system and the impact of attacks are well suited this study and examined in this paper. The predictability issues and the various attack strategies are also discussed. Based on KNN the robustness of the recommender system were examined and the sensitivity of the rating given by the users are also analyzed. Furthermore the robust PLSA also considered for the work
The Majority Rule: A General Protection on Recommender System
Recommender systems are widely used in a variety of scenarios, including online shopping, social network, and contents distribution. As users rely more on recommender systems for information retrieval, they also become attractive targets for cyber-attacks. The high-level idea of attacking a recommender system is straightforward. An adversary selects a strategy to inject manipulated data into the database of the recommender system to influence the recommendation results, which is also known as a profile injection attack. Most existing works treat attacking and protection in a static manner, i.e., they only consider the adversary’s behavior when analyzing the influence without considering normal users’ activities. However, most recommender systems have a large number of normal users who also add data to the database, the effects of which are largely ignored when considering the protection of a recommender system. We take normal users’ contributions into consideration and analyze popular attacks against a recommender system. We also propose a general protection framework under this dynamic setting
Single-User Injection for Invisible Shilling Attack against Recommender Systems
Recommendation systems (RS) are crucial for alleviating the information
overload problem. Due to its pivotal role in guiding users to make decisions,
unscrupulous parties are lured to launch attacks against RS to affect the
decisions of normal users and gain illegal profits. Among various types of
attacks, shilling attack is one of the most subsistent and profitable attacks.
In shilling attack, an adversarial party injects a number of well-designed fake
user profiles into the system to mislead RS so that the attack goal can be
achieved. Although existing shilling attack methods have achieved promising
results, they all adopt the attack paradigm of multi-user injection, where some
fake user profiles are required. This paper provides the first study of
shilling attack in an extremely limited scenario: only one fake user profile is
injected into the victim RS to launch shilling attacks (i.e., single-user
injection). We propose a novel single-user injection method SUI-Attack for
invisible shilling attack. SUI-Attack is a graph based attack method that
models shilling attack as a node generation task over the user-item bipartite
graph of the victim RS, and it constructs the fake user profile by generating
user features and edges that link the fake user to items. Extensive experiments
demonstrate that SUI-Attack can achieve promising attack results in single-user
injection. In addition to its attack power, SUI-Attack increases the
stealthiness of shilling attack and reduces the risk of being detected. We
provide our implementation at: https://github.com/KDEGroup/SUI-Attack.Comment: CIKM 2023. 10 pages, 5 figure
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
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