Combining Graph Contrastive Embedding and Multi-head Cross-Attention Transfer for Cross-Domain Recommendation

• Published in: Data Science and Engineering Vol. 8; no. 3; pp. 247 - 262
• Main Authors: Xiao, Shuo, Zhu, Dongqing, Tang, Chaogang, Huang, Zhenzhen
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2023; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Algorithm Analysis and Problem Complexity; Artificial Intelligence; Bias; Chemistry and Earth Sciences; Computer Science; Contrastive learning; Cross-domain recommendation; Data Mining and Knowledge Discovery; Database Management; Deep learning; Embedding; Knowledge management; Long-tail distribution; Motion picture directors & producers; Multi-head cross-attention; Physics; Popularity; Recommender systems; Research Paper; Sparsity; Statistics for Engineering; Systems and Data Security; Cross-domain recommendation; Long-tail distribution; Multi-head cross-attention; Contrastive learning

Cross-domain recommendation (CDR) has become an important research direction in the field of recommender systems due to the increasing demand for personalized recommendations across different domains. However, CDR faces multiple challenges, including data sparsity, popularity bias, and long-tail pro...