点击可以作弊：缓解点击诱饵问题的反事实建议
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
摘要：
推荐服务是信息系统中普遍存在的一项重要服务。为了向用户提供个性化建议，行业参与者采用了机器学习，更具体地说，是基于点击行为数据构建预测模型。这就是所谓的点击率（CTR）预测，它已经成为构建个性化推荐服务的黄金标准。然而，我们认为，点击和用户满意度之间存在显着的差距-这是常见的，用户被“欺骗”点击一个项目的有吸引力的标题/封面的项目。如果用户发现点击的项目的实际内容令人失望，这将严重损害用户对系统的信任。更糟糕的是，在这些有缺陷的数据上优化CTR模型将导致马太效应，使看似有吸引力但实际上质量较低的项目更频繁地被推荐。在本文中，我们制定的推荐模型作为一个因果图，反映了因果关系的因素，在推荐，并解决点击诱饵问题，通过执行反事实推理的因果图。我们想象一个反事实的世界，其中每个项目只有暴露特征（即，用户在做出点击决定之前可以看到的特征）。通过估计用户在反事实世界中的点击可能性，我们能够减少暴露特征的直接影响并消除点击诱饵问题。在真实数据集上的实验表明，该方法显著提高了CTR模型的点击后满意度。
英文摘要：
Recommendation is a prevalent and critical service in information systems. To provide personalized suggestions to users, industry players embrace machine learning, more specifically, building predictive models based on the click behavior data. This is known as the Click-Through Rate (CTR) prediction, which has become the gold standard for building personalized recommendation service. However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item. This will severely hurt user's trust on the system if the user finds the actual content of the clicked item disappointing. What's even worse, optimizing CTR models on such flawed data will result in the Matthew Effect, making the seemingly attractive but actually low-quality items be more frequently recommended. In this paper, we formulate the recommendation models as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph. We imagine a counterfactual world where each item has only exposure features (i.e., the features that the user can see before making a click decision). By estimating the click likelihood of a user in the counterfactual world, we are able to reduce the direct effect of exposure features and eliminate the clickbait issue. Experiments on real-world datasets demonstrate that our method significantly improves the post-click satisfaction of CTR 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。
