Session 4: Trustworthy Recommendation

Date: Wednesday September 20, 2:00 PM – 3:20 PM (GMT+8)
Room: Hall 406D
Session Chair: Michael Ekstrand
Parallel with: Session 3: Applications

• RESWhen Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation
  by Jiakai Tang (Gaoling School of Artificial Intelligence, Renmin University of China), Shiqi Shen (Wechat, Tencent, Beijing), Zhipeng Wang (Wechat, Tencent, Beijing), Zhi Gong (Wechat, Tencent, Beijing), Jingsen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China) and Xu Chen (Gaoling School of Artificial Intelligence, Renmin University of China).

  Fairness in the recommendation domain has recently attracted increasing attention due to the more and more concerns on the algorithm discrimination and ethics. While recent years have witnessed many promising fairness aware recommender models, an important problem has been largely ignored, that is, the fairness can be biased due to the user personalized selection tendencies or the non-uniform item exposure probabilities. To study this problem, in this paper, we formally define a novel task named as unbiased fairness aware Top-N recommendation. For solving this task, we firstly define an ideal loss function based on all the user-item pairs. Considering that, in real-world datasets, only a small number of user-item interactions can be observed, we then approximate the above ideal loss with a more tractable objective based on the inverse propensity score (IPS). Since the recommendation datasets can be noisy and quite sparse, which brings difficulties for accurately estimating the IPS, we propose to optimize the objective in an IPS range instead of a specific point, which improve the model fault tolerance capability. In order to make our model more applicable to the commonly studied Top-N recommendation, we soften the ranking metrics such as Precision, Hit-Ratio and NDCG to derive an fully differentiable framework. We conduct extensive experiments to demonstrate the effectiveness of our model based on four real-world datasets.

  Full text in ACM Digital Library

• RESTowards Robust Fairness-aware Recommendation
  by Hao Yang (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China), Zhining Liu (Ant Group), Zeyu Zhang (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China), Chenyi Zhuang (Ant Group) and Xu Chen (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China).

  Due to the progressive advancement of trustworthy machine learning algorithms, fairness in recommender systems is attracting increasing attention and is often considered from the perspective of users. Conventional fairness-aware recommendation models make the assumption that user preferences remain the same between the training set and the testing set. However, this assumption is disagreed with reality, where user preference can shift in the testing set due to the natural spatial or temporal heterogeneity. It is concerning that conventional fairness-aware models may be unaware of such distribution shifts, leading to a sharp decline in the model performance. To address the distribution shift problem, we propose a robust fairness-aware recommendation framework based on Distributionally Robust Optimization (DRO) technique. In specific, we assign learnable weights for each sample to approximate the distributions that leads to the worst-case model performance, and then optimize the fairness-aware recommendation model to improve the worst-case performance in terms of both fairness and recommendation accuracy. By iteratively updating the weights and the model parameter, our framework can be robust to unseen testing sets. To ease the learning difficulty of DRO, we use a hard clustering technique to reduce the number of learnable sample weights. To optimize our framework in a full differentiable manner, we soften the above clustering strategy. Empirically, we conduct extensive experiments based on four real-world datasets to verify the effectiveness of our proposed framework. For benefiting the research community, we have released our project at https://anonyrobfair.github.io/.

  Full text in ACM Digital Library

• RESTwo-sided Calibration for Quality-aware Responsible Recommendation
  by Chenyang Wang (Tsinghua University), Yankai Liu (China Mobile Research), Yuanqing Yu (Tsinghua University), Weizhi Ma (Tsinghua University), Min Zhang (Tsinghua University), Yiqun Liu (Tsinghua University), Haitao Zeng (China Mobile Research), Junlan Feng (China Mobile Research) and Chao Deng (China Mobile Research).

  Calibration in recommender systems ensures that the user’s interests distribution over groups of items is reflected with their corresponding proportions in the recommendation, which has gained increasing attention recently. For example, a user who watched 80 entertainment videos and 20 knowledge videos is expected to receive recommendations comprising about 80% entertainment and 20% knowledge videos as well. However, with the increasing calls for quality-aware responsible recommendation, it has become inadequate to just match users’ historical behaviors, which could still lead to undesired effects at the system level (e.g., overwhelming clickbaits). In this paper, we envision the two-sided calibration task that not only matches the users’ past interests distribution (user-level calibration) but also guarantees an overall target exposure distribution of different item groups (system-level calibration). The target group exposure distribution can be explicitly pursued by users, platform owners, and even the law (e.g., the platform owners expect about 50% knowledge video recommendation on the whole). To support this scenario, we propose a post-processing method named PCT. PCT first solves personalized calibration targets that minimize the changes in users’ historical interest distributions while ensuring the overall target group exposure distribution. Then, PCT reranks the original recommendation lists according to personalized calibration targets to generate both relevant and two-sided calibrated recommendations. Extensive experiments demonstrate the superior performance of the proposed method compared to calibrated and fairness-aware recommendation approaches. We also release a new dataset with item quality annotations to support further studies about quality-aware responsible recommendation.

  Full text in ACM Digital Library

• REPRecAD: Towards A Unified Library for Recommender Attack and Defense
  by Changsheng Wang (University of Science and Technology of China), Jianbai Ye (University of Science and Technology of China), Wenjie Wang (National University of Singapore), Chongming Gao (University of Science and Technology of China), Fuli Feng (University of Science and Technology of China) and Xiangnan He (University of Science and Technology of China)

  In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values. Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments. To address this, we propose RecAD, a unified library aiming at establishing an open benchmark for recommender attack and defense. RecAD takes an initial step to set up a unified benchmarking pipeline for reproducible research by integrating diverse datasets, standard source codes, hyper-parameter settings, running logs, attack knowledge, attack budget, and evaluation results. The benchmark is designed to be comprehensive and sustainable, covering both attack, defense, and evaluation tasks, enabling more researchers to easily follow and contribute to this promising field. RecAD will drive more solid and reproducible research on recommender systems attack and defense, reduce the redundant efforts of researchers, and ultimately increase the credibility and practical value of recommender attack and defense. The project and documents are released at https://github.com/gusye1234/recad.

  Full text in ACM Digital Library

Back to program