Session 13: Robust RecSys 1

Date: Thursday October 17, 10:35 AM – 10:55 AM (GMT+2)
Room: Petruzzelli Theater
Session Chair: Saikishore Kalloori

• RES 🕓5RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
  by Shuo Su (Kuaishou Technology), Xiaoshuang Chen (Kuaishou Technology), Yao Wang (Kuaishou Technology), Yulin Wu (Kuaishou Technology), Ziqiang Zhang (Tsinghua University), Kaiqiao Zhan (Kuaishou Technology), Ben Wang (Kuaishou Technology) and Kun Gai

  Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources. Recommending by user-wise result caches is widely used when the system cannot afford a real-time recommendation. However, it is challenging to allocate real-time and cached recommendations to maximize the users’ overall engagement. This paper shows two key challenges to cache allocation, i.e., the value-strategy dependency and the streaming allocation. Then, we propose a reinforcement prediction-allocation framework (RPAF) to address these issues. RPAF is a reinforcement-learning-based two-stage framework containing prediction and allocation stages. The prediction stage estimates the values of the cache choices considering the value-strategy dependency, and the allocation stage determines the cache choices for each individual request while satisfying the global budget constraint. We show that the challenge of training RPAF includes globality and the strictness of budget constraints, and a relaxed local allocator (RLA) is proposed to address this issue. Moreover, a PoolRank algorithm is used in the allocation stage to deal with the streaming allocation problem. Experiments show that RPAF significantly improves users’ engagement under computational budget constraints.

  Full text in ACM Digital Library

• RES 🕓15Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
  by Kaike Zhang (Chinese Academy of Sciences), Qi Cao (Chinese Academy of Sciences), Yunfan Wu (Chinese Academy of Sciences), Fei Sun (Chinese Academy of Sciences), Huawei Shen (Chinese Academy of Sciences) and Xueqi Cheng (Chinese Academy of Sciences)

  Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowing attackers to insert fake users into the system’s training data to skew the exposure of certain items, known as poisoning attacks. Adversarial training has emerged as a notable defense mechanism against such poisoning attacks within recommender systems. Existing adversarial training methods apply perturbations of the same magnitude across all users to enhance system robustness against attacks. Yet, in reality, we find that attacks often affect only a subset of users who are vulnerable. These perturbations of indiscriminate magnitude make it difficult to balance effective protection for vulnerable users without degrading recommendation quality for those who are not affected. To address this issue, our research delves into understanding user vulnerability. Considering that poisoning attacks pollute the training data, we note that the higher degree to which a recommender system fits users’ training data correlates with an increased likelihood of users incorporating attack information, indicating their vulnerability. Leveraging these insights, we introduce the Vulnerability-aware Adversarial Training (VAT), designed to defend against poisoning attacks in recommender systems. VAT employs a novel vulnerability-aware function to estimate users’ vulnerability based on the degree to which the system fits them. Guided by this estimation, VAT applies perturbations of adaptive magnitude to each user, not only reducing the success ratio of attacks but also preserving, and potentially enhancing, the quality of recommendations. Comprehensive experiments confirm VAT’s superior defensive capabilities across different recommendation models and against various types of attacks.

  Full text in ACM Digital Library

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