Research and Design of Intelligent Learning System Based on Recommendation Technology

Li, Hui; Zhang, Shu; Shi, Jun; Hu, Yun

doi:10.2316/j.2019.201-2968

Cited by 22 publications

(15 citation statements)

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“…Rather, scaling-up possibilities convert traditional learning spaces into more-inclusive and sustainable learning spaces to create learning organisations (Senge, 2006). Recommendation systems are being explored for: predicting student performance by mining educational data on students' records, their motivation and socioeconomic data (El Hajji et al, 2019); offering personalised recommendations and prototype designs of network teaching resource systems (Li et al, 2019;Zhang et al, 2019), and reducing failure (El Mrabet & Moussa, 2019).…”

Section: Introductionmentioning

confidence: 99%

Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution

Recalde

Martín

Agustí

et al. 2021

Learning Environ Res

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The advent of the Fourth Industrial Revolution is introducing developments in Artificial Intelligence, Internet of Things, and other technologies in different sectors of our society, including education. This reality leads to a paradigm shift in which web-based cyber-physical environments will shape future learning environments. Thus, learning becomes ubiquitous, and schools assume new roles with systemic changes in communication, administration and management, becoming learning organisations. The use of technologies aligned with pedagogical strategies and new methodologies must lead to more-personalised systems. In this article, a comprehensive definition of smart schools is proposed. Smart schools must be endowed with integral management systems, inclusive, sustainable, and adopt new learning methodologies and advances from Industry 4.0 in an efficient way. Despite this conception and because research, government policies and business projects are not always in line with research, there is a need for deeper knowledge of how schools are approaching their upcoming transformation. To illuminate this purpose, in this study 37 principals from primary and secondary schools in Catalonia were interviewed. Thematic analysis focusing on technological and pedagogical innovations, management systems, inclusion, and sustainability identified some analogies with related research, pointing out that schools are far from implementing advanced technologies. Inclusion is the most-respected element thanks to the existing government regulation. Sustainability is hardly considered because of a lack of economic resources, but several schools consider themselves green schools and exhibit environmental practices. Conclusions are drawn to show that, although schools are not yet prepared to cope with the Fourth Industrial Revolution, its impact relies on the technology’s level of maturity and ease of use, as well as stakeholders as policymakers.

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Section: Introductionmentioning

confidence: 99%

Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution

Recalde

Martín

Agustí

et al. 2021

Learning Environ Res

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“…It has a wide application prospect in the tourism market. Because of the large error of data calculation in traditional recommendation system, literature [3] proposes an intelligent recommendation system for tourist attractions based on collaborative filtering. e overall structure diagram of the system is designed.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recommendation Business Model of Tourism Enterprise Value Platform from the Perspective of Value Cocreation

Wang

Tang

2021

Complexity

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With the rapid development of China’s economy, people pay attention to their own quality of life, and tourism has become the first choice for people from all walks of life to relax themselves. Tourism travel has mainly developed from the form of travel agency registration to the form of online registration based on the network platform business model. Considering the value cocreation and the diversity of tourism enterprise platform, this paper puts forward the business model research of intelligent recommendation of tourism enterprise platform from the perspective of value cocreation. Firstly, the commonly used recommendation algorithms are introduced, which are collaborative filtering recommendation algorithm, content filtering recommendation algorithm, and association rule recommendation algorithm. Secondly, it analyzes the number of tourists and economic benefits of the business platform of tourism enterprises from April 2020 to April 2021 and also analyzes the business models of five modules under the tourism platform on different platforms. Finally, three recommendation algorithms are used to compare the comprehensive performance of five modules in different business models. Finally, we find that the rate of accuracy and recall of business is above 88%, which can have good economic benefits and provide customers with high-quality recommendation service and good satisfaction.

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“…With the exponential growth of internet information, people get more and more redundant information when they access webpages so that they cost a lot of time to filter information. To solve the problem of information overload, recommender systems have been developed to help users to easily find the items they enjoy [1], [2]. Recommender system is typically divided into content-based recommendation, collaborative-filtering recommendation, and hybrid recommendation [3].…”

Section: Introductionmentioning

confidence: 99%

The Hierarchical Model for News Recommendation

Hong¹,

Zheng²,

Wu³

et al. 2020

Mechatronic Systems and Control

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Latent factor model (LFM) is a classical model-based collaborativefiltering approach that explains the user-item association by characterizing both items and users on latent factors inferred from rating patterns. Due to high data volume, we consider whether it is reasonable that LFM is a linear model with interaction between users and items. Therefore, we propose a hierarchical model, which groups items or users by item features or user features, and establish a LFM on each class. To avoid overfitting and comparison, we use different kinds of regularization to punish the model due to different meanings of regularization. Then we apply the hierarchical model to news recommendation. We use latent dirichlet allocation (LDA) to analyse news, which gets the topic distribution of news that can be used as news feature. The experimental results show the superiority of the hierarchical model in the news recommendation system with higher accuracy than other algorithms.

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Research and Design of Intelligent Learning System Based on Recommendation Technology

Cited by 22 publications

References 0 publications

Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution

Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution

Research on Intelligent Recommendation Business Model of Tourism Enterprise Value Platform from the Perspective of Value Cocreation

The Hierarchical Model for News Recommendation

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