基于隐式反馈的去偏可解释成对排序
Debiased Explainable Pairwise Ranking from Implicit Feedback
摘要：
最近的工作在推荐系统中强调了公平的重要性，特别是在偏见和透明度，除了预测准确性。在本文中，我们专注于最先进的成对排名模型，贝叶斯个性化排名（BPR），它以前被发现在预测准确性方面优于逐点模型，同时也能够处理隐式反馈。具体来说，我们解决了BPR的两个局限性：（1）BPR是一个黑箱模型，不解释其输出，从而限制了用户对建议的信任，以及分析师审查模型输出的能力;（2）由于数据是非随机缺失（MNAR），BPR容易受到暴露偏差的影响。这种曝光偏差通常会转化为对最不受欢迎的项目的不公平，因为它们有被推荐系统曝光不足的风险。在这项工作中，我们首先提出了一种新的可解释的损失函数和相应的矩阵分解为基础的模型，称为可解释的贝叶斯个性化排名（EBPR），生成建议以及基于项目的解释。然后，我们从理论上量化额外的曝光偏差所造成的可解释性，并使用它作为基础，提出一个无偏估计的理想EBPR损失。其结果是一个排名模型，恰当地捕捉了去偏见和可解释的用户偏好。最后，我们对三个真实世界的数据集进行了实证研究，证明了我们提出的模型的优点。
英文摘要：
Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user's trust in the recommendations, and the analyst's ability to scrutinize a model's outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. The result is a ranking model that aptly captures both debiased and explainable user preferences. Finally, we perform an empirical study on three real-world datasets that demonstrate the advantages of our proposed models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
