To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. To select these effective items, it is essential to estimate the causal effect of recommendations. The real effective items are the ones which can contribute to purchase probability uplift. Nevertheless, it is difficult to obtain the real causal effect since we can only recommend or not recommend an item to a user at one time. Furthermore, previous works usually rely on the randomized controlled trial~(RCT) experiment to evaluate their performance. However, it is usually not practicable in the recommendation scenario due to its unavailable time consuming. To tackle these problems, in this paper, we propose a causal collaborative filtering~(CausCF) method inspired by the widely adopted collaborative filtering~(CF) technique. It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations. CausCF extends the classical matrix factorization to the tensor factorization with three dimensions -- user, item, and treatment. Furthermore, we also employs regression discontinuity design (RDD) to evaluate the precision of the estimated causal effects from different models. With the testable assumptions, RDD analysis can provide an unbiased causal conclusion without RCT experiments. Through dedicated experiments on both the public datasets and the industrial application, we demonstrate the effectiveness of our proposed CausCF on the causal effect estimation and ranking performance improvement.
翻译:为提高用户的经验和公司利润,现代工业建议系统通常着眼于选择最有可能与用户互动的项目(例如点击和购买),然而,它们忽略了用户甚至可以在没有建议的情况下购买这些物品的事实。为了选择这些有效的项目,必须估计建议产生的因果关系。真正有效的项目是有助于购买提高概率的方法。然而,很难取得真正的因果关系,因为我们只能同时向用户推荐或不推荐一个项目。此外,以往的工作通常依靠随机控制的试验~RCT(RCT)来评价其绩效。然而,在建议设想中,由于用户没有时间,购买这些物品通常不切实际。为了解决这些问题,我们在本文件中提议了一种由广泛采用的协作过滤~(CausCF)技术所启发的因果关系过滤方法。它基于这样一种想法,即类似的用户不仅可以同时向用户推荐项目有相似的品味,而且也可以在建议下产生类似的处理效果。CausicFCF将典型的矩阵因子化系数化扩展至Extorality,我们用三个层面来评估了Starviculd Strial D。此外,我们用数据模型来评估了一种不精确性分析。