Friday 31 January 2025
Researchers have been exploring ways to improve the performance of recommender systems, but a new study highlights the potential for malicious attacks on these systems. The researchers developed an influence function-based approach called INFAttack, which can effectively launch profile pollution attacks on sequential recommenders.
Recommender systems are designed to suggest products or services based on users’ past behavior and preferences. However, these systems can be vulnerable to manipulation by attackers who want to influence the recommendations made to users. Profile pollution is a type of attack where an attacker injects fake user profiles into the system in order to manipulate the recommendations.
The researchers developed INFAttack as a way to launch profile pollution attacks on sequential recommender systems. These systems use sequence data, such as browsing history or purchase records, to make recommendations. The attackers use influence functions to identify the most influential items in each user’s profile and then inject fake profiles that mimic these items.
The researchers tested INFAttack on five benchmark datasets and found that it was able to significantly improve the attack performance compared to other methods. They also evaluated the impact of the attacks on the recommendation performance of the systems and found that the attacks were effective at reducing the accuracy of the recommendations.
The study highlights the potential risks posed by profile pollution attacks on recommender systems. As these systems become increasingly important in our daily lives, it is essential to develop robust defense mechanisms to prevent these types of attacks.
In addition to developing INFAttack, the researchers also explored the impact of different hyperparameters on the attack performance and recommendation accuracy. They found that the number of injected items and the damping term used in the influence function both had a significant impact on the attack performance.
The study’s findings have important implications for the development of recommender systems. As these systems become increasingly complex, it is essential to develop robust defense mechanisms to prevent attacks like profile pollution. The researchers’ work provides valuable insights into the potential risks posed by these types of attacks and highlights the need for further research in this area.
The study’s results also have implications for the development of influence functions, which are used in many applications beyond recommender systems. The researchers’ work demonstrates the potential for influence functions to be used maliciously and highlights the need for further research into their security.
Overall, the study’s findings highlight the importance of developing robust defense mechanisms to prevent attacks on recommender systems. As these systems become increasingly important in our daily lives, it is essential to ensure that they are secure and trustworthy.
Cite this article: “Malicious Attacks on Recommender Systems: INFAttack and Its Consequences”, The Science Archive, 2025.
Recommender Systems, Profile Pollution, Attacks, Infattack, Influence Functions, Sequential Recommenders, Recommendation Accuracy, Defense Mechanisms, Security, Trustworthiness.
