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 …

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

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 …

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 system aims to recommend items or information that may interest users
based on their behaviors and preferences. However, there may be sampling selection bias …

Recommender systems often suffer from selection bias as users tend to rate their preferred
items. The datasets collected under such conditions exhibit entries missing not at random …

Data-driven recommender systems have demonstrated great success in various Web
applications owing to the extraordinary ability of machine learning models to recognize …

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 …