A learning-based anomaly detection framework for secure recommendation

Abstract

Recommendation systems are essential tools for suggesting items or information to users based on their preferences and behaviors, which have been widely applied in various online platforms and services to personalize user experiences, increase user engagement, and drive business growth. However, the security and efficacy of recommendation systems can be compromised if the input data is tainted by malicious users. One of the primary threats to recommendation systems is shilling attacks, which pose great challenges in handling various types of huge-volume data with anomaly detection techniques. In this paper, we propose a novel anomaly detection framework named LTHiForest with the use of the learning to hash based isolation forest. Then, we instantiate the generic framework with one concrete hashing mechanism, extended order preserving hashing, to illustrate the stages of our framework and verify its effectiveness in detecting various anomalies. The core idea of this instantiation is to learn from data to construct a better isolation forest structure than the state-of-the-art methods like iForest and LSHiForest, which can achieve robust detection of various anomaly types. Extensive experiments on both synthetic and real-world data sets demonstrate the robustness and effectiveness of our framework for recommendation systems.