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 …

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 …

Ratings of a user to most items in recommender systems are usually missing not at random
(MNAR), largely because users are free to choose which items to rate. To achieve unbiased …

In database of recommender systems, users' ratings for most items are usually missing,
resulting in selection bias when users selectively choose items to rate. To address this …

Recommender systems are designed to learn user preferences from observed feedback and
comprise many fundamental tasks, such as rating prediction and post-click conversion rate …

In online advertising, the sample selection bias problem is a major cause of inaccurate
conversion rate estimates. Current mainstream solutions only perform causality-based …

Postclick conversion rate (CVR) prediction is an essential task for e-commerce domains, but
it is affected by selection bias. Some existing research has attempted to reduce selection …

In real-world industrial scenarios, post-click conversion rate (CVR) prediction models are
trained offline based on click events and subsequently applied online to both clicked and …

Post-click conversion rate (CVR) is a reliable indicator of online customers' preferences,
making it crucial for developing recommender systems. A major challenge in predicting CVR …

Recommendation systems (RS) has become integral to numerous applications, ranging
from e-commerce to content streaming. A critical problem in RS is that the ratings are …