Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Wu-Dong Xi"'
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 52:6983-6997
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::882db1dd053583935fa360a2e33ff31a
https://doi.org/10.1109/tsmc.2021.3129261
https://doi.org/10.1109/tsmc.2021.3129261
Publikováno v:
IJCNN
The aim of session-based recommendation is to predict the next-clicked item based on the anonymous behavior sequence. The existing works on session-based recommendation mainly capture the user preference within an individual session. This paper propo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57740571947c93c9f8c0f603222d88da
https://doi.org/10.1109/ijcnn52387.2021.9533519
https://doi.org/10.1109/ijcnn52387.2021.9533519
Publikováno v:
IEEE transactions on neural networks and learning systems. 33(11)
To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error back
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f962e5099d0ed842af77837df5576d0
https://pubmed.ncbi.nlm.nih.gov/34081589
https://pubmed.ncbi.nlm.nih.gov/34081589
Publikováno v:
IJCAI
Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7922044c35d7428084d1a06915f4350d
https://doi.org/10.24963/ijcai.2019/542
https://doi.org/10.24963/ijcai.2019/542
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
Recently, some attempts have been made in introducing deep neural networks (DNNs) to recommender systems for generating more accurate prediction due to the nonlinear representation learning capability of DNNs. However, they inevitably result in high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22cb3eaaab53a4676d4478435300d76c
https://doi.org/10.1109/tkde.2020.3023976
https://doi.org/10.1109/tkde.2020.3023976
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