Abstract Domain adaptation (DA) is the topical problem of adapting models from labelled
source datasets so that they perform well on target datasets where only unlabelled or …

Cross-Domain Recommendation (CDR) has been proven to effectively alleviate the data
sparsity problem in Recommender System (RS). Recent CDR methods often disentangle …

Using the user's past activity across different domains, the cross-domain recommendation
(CDR) predicts the items that users are likely to click. Most recent studies on CDR model …

Data scarcity is a perpetual challenge of recommendation systems, and researchers have
proposed a variety of cross-domain recommendation methods to alleviate the problem of …

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

Cross-domain recommendation aims to utilize user behavior data across various domains or
applications to enhance the performance and personalization of recommendation systems …

As the Internet has become increasingly ubiquitous and information has experienced
explosive growth, recommendation systems have evolved to become an indispensable …

Data sparsity poses a persistent challenge in Recommender Systems (RS), driving the
emergence of Cross-Domain Recommendation (CDR) as a potential remedy. However …

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 …

Cross-domain recommendation aims to leverage heterogeneous information to transfers
knowledge from a data-sufficient domain (source domain) to a data-scarce domain (target …