Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

Propensity-based weighting methods have been widely studied and demonstrated
competitive performance in debiased recommendations. Nevertheless, there are still many …

Recommender systems are seen as an effective tool to address information overload, but it
is widely known that the presence of various biases makes direct training on large-scale …

In recommender systems, users always choose the favorite items to rate, which leads to data
missing not at random and poses a great challenge for unbiased evaluation and learning of …

Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

Post-click conversion rate (CVR) prediction is an essential task for discovering user interests
and increasing platform revenues in a range of industrial applications. One of the most …