Results 21 to 30 of about 6,993,713 (204)
Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping
Entropy, 2020 Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain.
Yongpeng Wang +3 more
doaj +1 more source
MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
Applied Sciences, 2023 Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches ...
Yicheng Di, Yuan Liu
doaj +1 more source
Contrastive Cross-Domain Sequential Recommendation [PDF]
, 2023 Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
Cao, Jiangxia +4 more
core +1 more source
Advances in Multimedia, 2022 In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products.
Meng Shi
doaj +1 more source
DaGzang: a synthetic data generator for cross-domain recommendation services [PDF]
PeerJ Computer Science, 2023 Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations.
Luong Vuong Nguyen +2 more
doaj +2 more sources
Privacy-Preserving Matrix Factorization for Cross-Domain Recommendation
IEEE Access, 2021 Cross-domain recommender systems are known to provide solutions to the cold start and data sparsity problems in recommender systems. This can be achieved by leveraging sufficient ratings and users’ profiles in one domain to enhance accurate ...
Taiwo Blessing Ogunseyi +2 more
doaj +1 more source
Selective Knowledge Transfer for Cross-Domain Collaborative Recommendation
IEEE Access, 2021 Data sparsity is a major challenge for collaborative filtering recommender systems. A promising solution is to utilize feedback or ratings from multiple domains to improve the performance of recommendations in a collective way, known as the cross-domain ...
Hongwei Zhang +2 more
doaj +1 more source
Cross-Domain Recommendation with Multiple Sources
, 2020 Data sparsity remains a challenging and common problem in real-world recommender systems, which impairs the accuracy of recommendation thus damages user experience. Cross-domain recommender systems are developed to deal with data sparsity problem through
Zhang, G +5 more
core +1 more source
Neighbor interaction-based personalised transfer for cross-domain recommendation
Connection Science, 2023 Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer ...
Kelei Sun +4 more
doaj +1 more source
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [PDF]
, 2022 Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR).
Yu, Jiadi +4 more
core +1 more source