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Data-driven recommender systems have demonstrated great successes in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (i.e., correlation) from the massive historical user behaviors. However, these models still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of spurious correlations brought by non-causal relations. In this light, embracing ca
In this tutorial, we aim to introduce the key concepts in causality and provide a systemic review of existing work on causal recommendation. We will introduce existing methods from two different causal frameworks --- the potential outcome framework and the structural causal model. We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. A comparison between the two lines of work will be provided to facilit
PhD Student University of Science and Technology of China
Xiangnan He's Homepage
Wenjie Wang's Homepage