Intelligent English resource recommendation and teaching effect based on symmetric SDAE collaborative filtering algorithm

Wu, Dan

doi:10.1007/s12652-021-03049-4

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…(xii) Dan [17]: this work provided a research of the symmetric SDAE collaborative filtering algorithm which was conducted in order to build an intelligent English resource recommendation system. (xiii) Wanni [18]: a sophisticated basic grammar detection system was built using a mix of evolutionary and KNN algorithms.…”

Section: Related Workmentioning

confidence: 99%

Research on the Hierarchical Teaching Method of University English Based on Artificial Intelligence Wireless Network

Zhang

2022

Wireless Communications and Mobile Computing

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University English is one of the basic core courses for all university students. The course involves many difficult concepts and grammatical structures, so it is difficult to most students. The starting point in any teaching program is to determine whether teaching is needed to specify what that teaching should accomplish. So the students’ need is of vital importance to the success of university English teaching. Traditional university English teaching does not take into account students’ individual learning abilities and feedback, which would cause students to not grasp enough the key knowledge, and then make students lose their interest in English. The development of artificial intelligence technology is becoming more and more mature, especially in the application of English teaching, which promotes the reform, development, and modernization of English teaching. Hence, in this paper, we propose a hierarchical teaching method for the university English teaching platform and employ artificial intelligence to find the needs of university students and know about the mastering knowledge of students. Initially, the dataset is preprocessed using normalization, and then, the feature extraction is performed using principal component analysis (PCA). For classification of the data, we employ the K -means clustering algorithm. To enhance the evaluation system, the whale optimization algorithm (WOA) is used. The performance of the proposed system is analyzed, and it is shown that the average score of students who used the proposed platform to learn university English is far higher than those students who did not use the platform. Hence, the platform can improve students’ autonomous learning and English abilities.

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Section: Related Workmentioning

confidence: 99%

Research on the Hierarchical Teaching Method of University English Based on Artificial Intelligence Wireless Network

Zhang

2022

Wireless Communications and Mobile Computing

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“…focus area, user criteria etc.) (4); these were categorised into two groups based on technique/ method and application (5); research trends and challenges were identified for each application (6); finally, research objectives and questions were devised based on the review findings (7).…”

Section: Proposed Systemmentioning

confidence: 99%

An Intelligent Destination Recommender System for Tourists

2023

IRJMETS

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Tourism has become a major industry worldwide, with people traveling more frequently than ever before. With so many destinations to choose from, travelers often find it challenging to identify new and exciting places to visit. This paper presents a machine learning-based tourist recommender system that uses Random Forest, XGBoost, and Gradient Boosting algorithms to recommend new destinations to users based on their preferences and travel history.The proposed system uses a large dataset of tourist destinations, including their cultural, historical, and recreational attractions, to generate personalized recommendations for travelers. The system considers various factors, such as the user's budget, travel history, and preferred activities, to identify destinations that are likely to be of interest. The system's accuracy is tested using real-world data, and the results demonstrate that the proposed approach is highly effective in identifying new and exciting destinations that match the user's preferences.The system's implementation demonstrates the power of machine learning algorithms in enhancing the user's travel experience by helping them discover new and exciting destinations. The proposed system can be integrated into various travel platforms, including travel websites and travel agencies, to provide personalized recommendations to users. The system's flexibility and ability to adapt to changing user preferences make it an ideal tool for travelers looking to explore new destinations.

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Electronic Information Professional English Learning System Using Recurrent Neural Network-Collaborative Filtering Recommendation Algorithm

2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

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Intelligent English resource recommendation and teaching effect based on symmetric SDAE collaborative filtering algorithm

Cited by 3 publications

References 23 publications

Research on the Hierarchical Teaching Method of University English Based on Artificial Intelligence Wireless Network

Research on the Hierarchical Teaching Method of University English Based on Artificial Intelligence Wireless Network

An Intelligent Destination Recommender System for Tourists

Electronic Information Professional English Learning System Using Recurrent Neural Network-Collaborative Filtering Recommendation Algorithm

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