Recommendation system using hybrid collaborative filtering methods for community searching

Eriya,; Kodu, P. G.; Nugrahani, F.; Ghosh, Arko

doi:10.1088/1742-6596/1193/1/012021

Cited by 3 publications

(2 citation statements)

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“…As modern science and technology develop rapidly, the Internet and artificial intelligence and other science and technology are reshaping educational forms such as teacher education development, and the development of network technology also provides data support for English teachers' professional development planning [1]. Based on data mining technology applied to teachers' professional education, through the collection of statistical teaching big data, deep learning and intelligent analysis of teachers' characteristics, it can provide personalized guidance solutions for teachers' quality improvement [2]. The career development stages of English teachers are generally divided into career exploration stage, career establishment stage, mid-career stage and late career stage [3].…”

Section: Introductionmentioning

confidence: 99%

Prediction of English Teacher Career Development Based on Data Mining and Time Series Model

Fan

2024

SCPE

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With the gradual growth of the teaching profession, the teaching profession is facing new trends in reform and development, and the same dilemma exists for English teachers’ career development and planning. To this end, the study first uses a modified K-means clustering method to cluster and analyse the factors affecting English teachers’ professional development, forming a system of indicators on English teachers’ professional development. The Long Short-Term Memory (LSTM) network employed the time-series features to create a time-series model, and the Support Vector Machine (SVM) was used to forecast the course of English teachers’ career development. To assess the current career status of English teachers and their impact on people and organizations, this study proposes a career prediction model for English teachers. This model utilizes data mining and time series modeling to provide accurate predictions. In accordance with the experimental findings, the precision of the upgraded K-Means model was 98.58%, and the error between the projected sample data and the actual sample data for the training of the trend prediction model for English teachers’ career growth was 0.032. It was able to accurately predict teachers’ career development and explore the specific factors affecting English teachers’ career development, so as to solve the problems in teachers’ career development.

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Section: Introductionmentioning

confidence: 99%

Prediction of English Teacher Career Development Based on Data Mining and Time Series Model

Fan

2024

SCPE

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“…During the COVID-19 pandemic, recommendation systems were used to calculate the risk levels of various regions by considering several criteria [9]. In local environments, recommendation systems are also useful, for example, in determining nearby communities based on location, category, and interests [10].…”

Section: Introductionmentioning

confidence: 99%

Recommendation System for Selecting Web Programming Learning Materials for Vocational High School Students using Multi-criteria Recommendation Systems

Wahyuliningtyas,

Arif,

Kusumawati

2024

International Journal of Advances in Data and Information Syste

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In the independent curriculum, the learning that is carried out focuses on developing character, student competence and honing interests, talents. So the amount of learning material given to students does not have to be complete or less. Apart from that, the independent curriculum no longer burdens students with achieving a minimum score because assessments no longer use Minimum Completeness Criteria (KKM) scores. This makes it difficult for teachers to determine whether the material that has been explained can be understood because grades are not a benchmark for a student's success. In fact, if the teacher does not know a student's understanding, the teacher will have difficulty continuing to the next material. Implementation of the Multi-Criteria Recommender System (MCRS) can make it easier for teachers to predict whether students can progress to the next material and recommend which modules are suitable for these students. The recommendation system that will be built is in the form of web-based learning media so that students can be more interested and can help teachers improve learning outcomes. The method used is collaborative filtering by comparing adjusted cosine similarity, cosine based similarity and spearman rank order correlation. Based on the implementation of MCRS using the collaborative filtering method, it shows that the results of the recommendation system have a good impact on the teaching and learning process. Based on the 3 algorithms implemented, the best prediction result is cosine based similarity because the MAE value obtained is the lowest, namely 1.19 and the accuracy value is 76%.

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Music recommendation algorithm based on user portrait

2021

2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (

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Recommendation system using hybrid collaborative filtering methods for community searching

Cited by 3 publications

References 0 publications

Prediction of English Teacher Career Development Based on Data Mining and Time Series Model

Prediction of English Teacher Career Development Based on Data Mining and Time Series Model

Recommendation System for Selecting Web Programming Learning Materials for Vocational High School Students using Multi-criteria Recommendation Systems

Music recommendation algorithm based on user portrait

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