A resource recommendation method based on dynamic cluster analysis of application characteristics

Oh, Yoori; Kim, Chongam

doi:10.1007/s10586-018-2829-0

Cited by 9 publications

(5 citation statements)

References 6 publications

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Section: Introductionmentioning

confidence: 99%

“…In order to solve this problem, the World Environment and Development Committee officially proposed the concept of sustainable development in 1987 and advocated the protection of the ecological environment while promoting economic growth. This concept has become a common claim of all countries in the world 1–3 …”

Section: Introductionmentioning

confidence: 99%

“…This concept has become a common claim of all countries in the world. [1][2][3] In 2014, only eight of China's 74 major cities achieved air quality standards. The average annual concentration of PM2.5 in Chinese cities is 65 μg/m 3 , and the PM2.5 levels in some northern cities are above 100 μg/m 3 , far exceeding the World Health Organization Phase II standard (25 μg/m 3 ).…”

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confidence: 99%

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Retracted: Green intelligent financial system construction paradigm based on deep learning and concurrency models

Feng

Shi

Wang

et al. 2020

Concurrency and Computation

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Green finance represents a new trend and new direction for future financial development, and it is an innovation and change in the financial field. The role of the financial market in environmental protection has gradually become the consensus of the financial community. Although the total amount of environmental protection investment in China shows a growing trend, the actual environmental protection investment still has a large gap compared with the increasing capital demand for environmental protection work. Financial resources play a key role in resource allocation. As long as funds are gradually withdrawn from polluting industries, they will be more invested in green and environmental industries, and resources such as land and labor will be optimally allocated. Faced with the complex international and domestic economic environment and increasing environmental pressures, this article proposes the green intelligent financial system construction paradigm based on deep learning and concurrency models.The deep learning model specialized on convolutional neural networks (CNN) is applied to preprocess the information, the data cleaning and vision models are integrated to extract the structure data. The concurrency model is applied to guarantee the efficiency of the system. The experimental results compared with the state-of-the-art models have reflected the robustness of the proposed framework. K E Y W O R D Sconcurrency model, deep learning model, interaction design, neural network, structural design INTRODUCTIONSince the industrial revolution, the developed countries and newly industrialized countries have experienced rapid economic growth and their living standards have been greatly improved, but they have followed serious environmental problems. Under the traditional development model, economic growth is at the cost of huge resource consumption, neglecting the control of pollutant emissions, making resources increasingly tight, environmental pollution is serious, the ecological environment is deteriorating, and human living environment is facing great challenges. In order to solve this problem, the World Environment and Development Committee officially proposed the concept of sustainable development in 1987 and advocated the protection of the ecological environment while promoting economic growth. This concept has become a common claim of all countries in the world. [1][2][3] In 2014, only eight of China's 74 major cities achieved air quality standards. The average annual concentration of PM2.5 in Chinese cities is 65 μg/m 3 , and the PM2.5 levels in some northern cities are above 100 μg/m 3 , far exceeding the World Health Organization Phase II standard (25 μg/m 3 ). The water pollution in China's drinking water sources exceeds 75%, and more than 19% of the cultivated land area is over-contaminated.China's severe environmental pollution is largely related to highly polluting industrial structures, energy structures, and transportation structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Retracted: Green intelligent financial system construction paradigm based on deep learning and concurrency models

Feng

Shi

Wang

et al. 2020

Concurrency and Computation

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“…Online learning resource recommendation is actually a disclassification problem of predicting whether a learner will learn course [18]. Online learning resource recommendation is actually a disclassification problem of predicting whether a learner will learn course [18,19]. If the missing samples in the dataset are taken by default to positive samples, it can easily lead to an unbalanced dataset.…”

Section: Dataset Constructionmentioning

confidence: 99%

Deep Collaborative Online Learning Resource Recommendation Based on Attention Mechanism

Hao

Yang

2022

Scientific Programming

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In view of the lack of hierarchical and systematic resource recommendation caused by rich online learning resources and many learning platforms, an attention-based ADCF online learning resource recommendation model is proposed by introducing the attention mechanism into a deep collaborative DCF model. Experimental results show that the proposed ADCF model enables an accurate recommendation of online learning resources, reaching 0.626 and 0.339 on the HR and NDCG metrics, respectively, compared to the DCF models before improved, up by 1.31% and 1.25%, and the proposed ADCF models by 1.79%, 2.17%, and 2.32%, respectively, compared to the IUNeu and NeuCF models.

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“…A mature English online learning platform should be able to meet students' demand for learning resources, improve their access efficiency to each learning project, and optimize their learning experience. Now a few resource recommendation systems that can effectively alleviate the problems of large resource volume and slow screening speed have been applied to various Internet platforms [4][5][6][7][8]. The basic idea of these resource recommendation systems is to mine the data of students' preferences or abilities based on their operation history or evaluation information to further realize the prediction and recommendation services of their requirements and interests [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

Zhou

2021

Int. J. Emerg. Technol. Learn.

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Mature online English learning platforms should provide students with necessary learning resources, ensure efficient access to learning projects, and offer the optimal learning experience. However, the traditional recommendation methods for English learning resources cannot satisfy the in-depth learning demand of students. To solve the problem, this paper designs a resource recommendation algorithm for online English learning systems based on learning ability evaluation. Firstly, the workflow of the designed algorithm was introduced, and a four-layer test system was developed for students’ English learning ability evaluation. Next, an English learning ability evaluation method was proposed based on the maximum expectation algorithm, as well as the estimation methods for parameters like learning ability, degree of discrimination, difficulty, and guess coefficient. Experimental results demonstrated the good effect of the proposed resource recommendation algorithm. The research findings provide a reference for resource recommendation of other online learning systems.

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A resource recommendation method based on dynamic cluster analysis of application characteristics

Cited by 9 publications

References 6 publications

Retracted: Green intelligent financial system construction paradigm based on deep learning and concurrency models

Retracted: Green intelligent financial system construction paradigm based on deep learning and concurrency models

Deep Collaborative Online Learning Resource Recommendation Based on Attention Mechanism

A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

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