Zobrazeno 1 - 10
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pro vyhledávání: '"Dong, Ruihai"'
Autor:
Liu, Dairui, Du, Honghui, Yang, Boming, Hurley, Neil, Lawlor, Aonghus, Li, Irene, Greene, Derek, Dong, Ruihai
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these models, we introduce Transformers4NewsRec, a new Python framework built o
Externí odkaz:
http://arxiv.org/abs/2410.13125
Accurate and robust stock trend forecasting has been a crucial and challenging task, as stock price changes are influenced by multiple factors. Graph neural network-based methods have recently achieved remarkable success in this domain by constructin
Externí odkaz:
http://arxiv.org/abs/2410.07216
Autor:
Ruan, Qin, Xu, Jin, Dong, Ruihai, Younus, Arjumand, Mai, Tai Tan, O'Sullivan, Barry, Leavy, Susan
Societal risk emanating from how recommender algorithms disseminate content online is now well documented. Emergent regulation aims to mitigate this risk through ethical audits and enabling new research on the social impact of algorithms. However, th
Externí odkaz:
http://arxiv.org/abs/2409.12396
Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative representations for assets can be used for tasks like sector classifica
Externí odkaz:
http://arxiv.org/abs/2407.18645
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate o
Externí odkaz:
http://arxiv.org/abs/2401.16298
Autor:
Liu, Dairui, Yang, Boming, Du, Honghui, Greene, Derek, Lawlor, Aonghus, Dong, Ruihai, Li, Irene
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 ha
Externí odkaz:
http://arxiv.org/abs/2312.10463
Autor:
Du, Haiwen, Ju, Zheng, An, Yu, Du, Honghui, Zhu, Dongjie, Tian, Zhaoshuo, Lawlor, Aonghus, Dong, Ruihai
Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer fro
Externí odkaz:
http://arxiv.org/abs/2308.01138
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic info
Externí odkaz:
http://arxiv.org/abs/2307.06576
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the coherence of keywords within topics, resulting in low explainability of
Externí odkaz:
http://arxiv.org/abs/2306.07403
Publikováno v:
ACM Trans. Recomm. Syst. 1, 1, Article 1 (January 2024), 26 pages.
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the
Externí odkaz:
http://arxiv.org/abs/2306.07506