Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

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

Accurate estimation of post-click conversion rate is critical for building recommender
systems, which has long been confronted with sample selection bias and data sparsity …

Effectively representing users lie at the core of modern recommender systems. Since users'
interests naturally exhibit multiple aspects, it is of increasing interest to develop multi-interest …

Post-click conversion rate (CVR) estimation is a fundamental task in developing effective
recommender systems, yet it faces challenges from data sparsity and sample selection bias …

Most recommender systems optimize the model on observed interaction data, which is
affected by the previous exposure mechanism and exhibits many biases like popularity bias …

Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …

Implicit feedback (eg, user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …

In collaborative filtering, the quality of recommendations critically relies on how easily a
model can find similar users for a target user. Hence, a niche user who prefers items out of …

Debiased recommendation with a randomized dataset has shown very promising results in
mitigating system-induced biases. However, it still lacks more theoretical insights or an ideal …