To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

As multimedia systems like Tiktok and Youtube become increasingly prevalent, there is a
growing demand for effective recommendation techniques. However, current …

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have
been proposed to improve the recommendation accuracy in a target dataset …

Contrastive learning with Transformer-based sequence encoder has gained predominance
for sequential recommendation. It maximizes the agreements between paired sequence …

Cross-domain recommendation is an important method to improve recommender system
performance, especially when observations in target domains are sparse. However, most …

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring
knowledge from an informative source domain to the target domain, which inevitably …

Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of
data sparsity and cold start by transferring general knowledge from the source to the target …

The cold-start problem, faced when providing recommendations to newly joined users with
no historical interaction record existing in the platform, is one of the most critical problems …

The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the
recommendation performance on a sparser target domain by transferring the knowledge …