Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

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

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …

We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …

Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using
data generated by a different policy. Because of its huge potential impact in practice, there …

In real-world recommender systems and search engines, optimizing ranking decisions to
present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking …

Counterfactual estimators enable the use of existing log data to estimate how some new
target recommendation policy would have performed, if it had been used instead of the …

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 …

The real-world recommender system needs to be regularly retrained to keep with the new
data. In this work, we consider how to efficiently retrain graph convolution network (GCN) …