Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Fulan Qian"'
Publikováno v:
International Journal of Molecular Sciences, Vol 15, Iss 7, Pp 12731-12749 (2014)
Protein–protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in fu
Externí odkaz:
https://doaj.org/article/e4b76a7c5cdf41fb80d9c4af2af1e32c
Publikováno v:
ACM Transactions on Information Systems. Jan2024, Vol. 42 Issue 1, p1-27. 27p.
Publikováno v:
IEEE Transactions on Big Data. :1-12
Publikováno v:
Knowledge and Information Systems.
Autor:
Binfeng Huang, Fulan Qian
Publikováno v:
International Conference on Electronic Information Engineering and Computer Science (EIECS 2022).
Publikováno v:
2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).
Publikováno v:
Neurocomputing. 458:195-203
Recommender system uses user-item historical interactions to portray user preferences. Due to the problem of data sparseness, auxiliary information is introduced to describe user preferences, such as user/item attribute information. However, some of
Publikováno v:
Neurocomputing. 453:524-537
Graph convolutional neural networks (GCNs) based on spectral-domain have achieved impressive performance for semi-supervised node classification task. Recently, graph wavelet neural network (GWNN) has made a significant improvement for this task. How
Massive information often make users lose their focuses so that desired targets are missed. Recommendation system can efficiently provide interesting information for users in the case of "information overload". Most existing recommendation algorithms
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::facdaf6b5d677af2798305cbc4aa840b
https://doi.org/10.21203/rs.3.rs-2206282/v1
https://doi.org/10.21203/rs.3.rs-2206282/v1
Publikováno v:
International Journal of Machine Learning and Cybernetics. 12:1993-2005
Latent factor models (LFMs) have been widely applied in many rating recommendation systems because of their prediction rating capability. Nevertheless, LFMs may not fully leverage rating information and lack good recommendation performance. Furthermo