Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Xu, Gongwen; Jia, Guangyu; Shi, Lin; Zhang, Zhijun

doi:10.1155/2021/9590502

Cited by 35 publications

(22 citation statements)

References 35 publications

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“…According to the exploratory data analysis, we can say that the university’s admission campaign ( Wang 2022 ) successfully complies with the Pareto rule ( Xu et al 2021 ): about 80% of applicants apply for and get admitted to about 20% of the most popular specialties. Similar findings in education with this rule are represented in several recent studies ( Yahya and Osman 2019 ; Zhong and Ding 2022 ; Y. Zhou et al 2018 ; J. Zhou et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Федушко

Ustyianovych²,

Syerov³

2022

J. Intell.

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In this article, we provide an approach to solve the problem of academic specialty selection in higher educational institutions with Ukrainian entrants as our target audience. This concern affects operations at universities or other academic institutions, the labor market, and the availability of in-demand professionals. We propose a decision-making architecture for a recommendation system to assist entrants with specialty selection as a solution. The modeled database is an integral part of the system to provide an in-depth university specialties description. We consider developing an API to consume the data and return predictions to users in our future studies. The exploratory data analysis of the 2021 university admission campaign in Ukraine confirmed our assumptions and revealed valuable insights into the specifics of specialty selection among entrants. We developed a comprehension that most entrants apply for popular but not necessarily in-demand specialties at universities. Our findings on association rules mining show that entrants are able to select alternative specialties adequately. However, it does not lead to successful admission to a desired tuition-free education form in all cases. So, we find it appropriate to deliver better decision-making on specialty selection, thus increasing the likelihood of university admission and professional development based on intelligent algorithms, user behavior analytics, and consultations with academic and career orientation experts. The results will be built into an intelligent virtual entrant’s assistant as a service.

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Section: Discussionmentioning

confidence: 99%

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Федушко

Ustyianovych²,

Syerov³

2022

J. Intell.

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“…A recall describes the model's success in finding the information again. Precision and recall can be calculated as formulas 13 and 14 [20].…”

Section: Performance Evaluationmentioning

confidence: 99%

Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification

RIfai

Setiawan²

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95%.

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“…This retrieval methods could be divided into two types of representation, binary and real-value [8,9]. There are some popular real-value representation, such as deep learning methods [10], dictionary learning methods [11,12] and graph correlation methods [13,14]. These methods have high accuracy and are widely used.…”

Section: Related Workmentioning

confidence: 99%

Bagging-based cross-media retrieval algorithm

Yin²,

Hong³

et al. 2022

Soft Comput

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It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.

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Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Cited by 35 publications

References 35 publications

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification

Bagging-based cross-media retrieval algorithm

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