Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize
different domain knowledge and users' historical behaviors for the next-item prediction. In …

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions
based on user's historical sequential interactions from multiple domains. Generally, a key …

In modern recommender systems, sequential recommendation leverages chronological user
behaviors to make effective next-item suggestions, which suffers from data sparsity issues …

Cross-domain recommendation (CDR), which leverages information collected from other
domains, has been empirically demonstrated to effectively alleviate data sparsity and cold …

Cross-domain Recommendation (CR) has been extensively studied in recent years to
alleviate the data sparsity issue in recommender systems by utilizing different domain …

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users'
sequential preferences across multiple recommendation domains. While significant …

Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …

Cross-domain recommendation (CDR) aims at leveraging the correlation of users' behaviors
in both the source and target domains to improve the user preference modeling in the target …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …

Cross domain recommendation (CDR) is one popular research topic in recommender
systems. This article focuses on a popular scenario for CDR where different domains share …