Zobrazeno 1 - 10
of 443
pro vyhledávání: '"cross-domain recommendation"'
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 6, Pp 7877-7892 (2024)
Abstract Cross-domain recommendation aims to integrate data from multiple domains and introduce information from source domains, thereby achieving good recommendations on the target domain. Recently, contrastive learning has been introduced into the
Externí odkaz:
https://doaj.org/article/d44c7aa4aa8b4e519da947d79014fa48
Autor:
WANG Yonggui, LIU Danni
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 7, Pp 1792-1805 (2024)
A cross-domain recommendation algorithm combining multi-personalized bridges and self-supervised learning (MS-PTUPCDR) is proposed for users with less project interaction in the target domain in the cross-domain recommendation system. Firstly, a vari
Externí odkaz:
https://doaj.org/article/0b45276b0bd94c9e9a2f11600c1ad33c
Publikováno v:
Data Technologies and Applications, 2023, Vol. 58, Issue 2, pp. 293-317.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/DTA-03-2023-0101
Autor:
Weichen Wang, Jing Wang
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 4939-4954 (2024)
Abstract The sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved the prediction accuracy
Externí odkaz:
https://doaj.org/article/064e4ab16df04eb0ab14e46865ebe91a
Publikováno v:
Data Science and Engineering, Vol 9, Iss 2, Pp 238-249 (2024)
Abstract The cold-start problem in recommender systems has been facing a great challenge. Cross-domain recommendation can improve the performance of cold-start user recommendations in the target domain by using the rich information of users in the so
Externí odkaz:
https://doaj.org/article/edc8b16f8d96431e9ec53fe2419fa094
Publikováno v:
Data Science and Engineering, Vol 8, Iss 3, Pp 247-262 (2023)
Abstract 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,
Externí odkaz:
https://doaj.org/article/0c13ca2c0f494845aa9e47ff5a21dbd9
Publikováno v:
IEEE Access, Vol 11, Pp 90724-90738 (2023)
Recent advancements in the domain of recommender systems have stemmed from the inspiration of representing the user-item interaction into graphs. These heterogeneous graphs comprehensively capture the non-linear relationships between users and items
Externí odkaz:
https://doaj.org/article/dc1350b5154f4af182e7348da706b405
Autor:
ZHANG Jia, DONG Shou-bin
Publikováno v:
Jisuanji kexue, Vol 49, Iss 9, Pp 41-47 (2022)
In order to solve the user cold-start problem caused by data-sparse in recommender system,this paper proposes a cross-domain recommendation algorithm based on aspect-level user preference transfer,named CAUT.CAUT is devised to learn aspect transfer a
Externí odkaz:
https://doaj.org/article/f10d354f2cd446d28bf8d62ef55267e1
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