CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
This work addresses the need for causal effect estimation in recommender systems to identify items that genuinely increase purchase probability, which is crucial for improving user experience and corporate profits, though it is incremental as it builds on existing collaborative filtering techniques.
The paper tackles the problem of estimating the causal effect of recommendations in collaborative filtering, which is difficult due to the inability to simultaneously recommend and not recommend items, and proposes CausCF, a method that extends matrix factorization to tensor factorization with user, item, and treatment dimensions, demonstrating effectiveness in causal effect estimation and ranking performance improvement on public datasets and industrial applications.
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.