Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Richong Zhang"'
Publikováno v:
IEEE Access, Vol 8, Pp 108300-108309 (2020)
User interests modeling has been exploited as a critical component to improve the predictive performance of recommender systems. However, with the absence of explicit information to model user interests, most approaches to recommender systems exploit
Externí odkaz:
https://doaj.org/article/33037f1004bb4be891d16a41a5d81215
Autor:
Richong Zhang, Yongyi Mao
Publikováno v:
IEEE Access, Vol 7, Pp 13189-13199 (2019)
The success of the probabilistic matrix factorization (PMF) model has inspired the rapid development of collaborative filtering algorithms, among which timeSVD++ has demonstrated great performance advantage in solving the movie rating prediction prob
Externí odkaz:
https://doaj.org/article/cea8e4af1ca64fd5b24345b825f38a30
Publikováno v:
IEEE Access, Vol 7, Pp 12511-12520 (2019)
The working of recurrent neural networks has not been well understood to date. The construction of such network models, hence, largely relies on heuristics and intuition. This paper formalizes the notion of “memory length” for recurrent networks
Externí odkaz:
https://doaj.org/article/20a8570bf11543c7919b6aace40244a9
Publikováno v:
ACM Transactions on Information Systems. 41:1-25
Each link prediction task requires different degrees of answer diversity. While a link prediction task may expect up to a couple of answers, another may expect nearly a hundred answers. Given this fact, the performance of a link prediction model can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::164762188f4c4691fb9bd23636db8834
https://doi.org/10.1145/3533769
https://doi.org/10.1145/3533769
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61e369d16dc4cb6030f63ae8211de73a
https://doi.org/10.1109/tkde.2023.3238993
https://doi.org/10.1109/tkde.2023.3238993
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:11730-11738
Unsupervised sentence representation learning is a fundamental problem in natural language processing. Recently, contrastive learning has made great success on this task. Existing constrastive learning based models usually apply random sampling to se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05e8679345407effdd38b35d2fb7577a
https://doi.org/10.1609/aaai.v36i10.21428
https://doi.org/10.1609/aaai.v36i10.21428
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd0b6b978becd44a01da9a0e8f898261
http://arxiv.org/abs/2305.09269
http://arxiv.org/abs/2305.09269
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encounter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d5f5e91a0357183e35f19df0aad31f0
https://doi.org/10.1145/3543507.3583325
https://doi.org/10.1145/3543507.3583325
Autor:
Xuefeng Zhang, Richong Zhang, Xiaoyang Li, Fanshuang Kong, Junfan Chen, Samuel Mensah, Yongyi Mao
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7db32aaa434653eac86a0de46554670a
https://doi.org/10.1145/3543507.3583191
https://doi.org/10.1145/3543507.3583191
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 30:2568-2583
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9233d9904c782e30bf1b2a8327448a17
https://doi.org/10.1109/taslp.2022.3190731
https://doi.org/10.1109/taslp.2022.3190731