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Baba MBAYE

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Papers by Baba MBAYE

Research paper thumbnail of Enriched Semantic Recommender System For Education

Research paper thumbnail of Enriched semantic recommendation system : application to the e-marketing domain

HAL (Le Centre pour la Communication Scientifique Directe), Apr 25, 2022

Research paper thumbnail of Representation of Knowledge Extracted from the Digital Booklet Data for the Work-Study Program

Research paper thumbnail of Studea: Digital Booklet for Apprenticeship

Research paper thumbnail of Semantic Recommender System Using Machine Learning for Education

Research paper thumbnail of Recommender System Using Unsupervised Machine Learning for Satisfaction Surveys

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; and Theory and Practice in Modern Computing 2019, 2019

Satisfaction surveys are being used more and more by companies to improve their sales force. With... more Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of new technologies the piloting of these satisfaction surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the recommendation system and to make the system partially autonomous on decision-making (to less involving the recommendation engine). Our approach is divided into a) the recommendation process for decision-making, b) unsupervised ML and c) partial "empowerment" for decision-making.

Research paper thumbnail of Organisation of Knowledge from Traces of Human Learning

Proceedings of the International Conference on e-Learning 2019, 2019

Research paper thumbnail of Recommender System: Collaborative Filtering of e-Learning Resources

The significant amount of information available on the web has led to difficulties for the learne... more The significant amount of information available on the web has led to difficulties for the learner to find useful information and relevant resources to carry out their training. The recommender systems have achieved significant success in the area of e-commerce, they still have difficulties in formulating relevant recommendations on e-learning resources because of the different characteristics of learners. Most of the existing recommendation techniques do not take these characteristics into account. This problem can be mitigated by including learner information in the referral process. Currently many recommendation techniques have cold start problems and classification problems. In this paper, we propose an ontology-based collaborative filtering recommendation system for recommending learners' online learning resources based on a decision algorithm (DA). In our approach, ontology is used to model and represent domain knowledge about the learner and learning resources. Our approa...

Research paper thumbnail of Machine Learning 2019: Collaborative filtering combined with machine learning for satisfaction surveys - Baba MBAYE - University of Franche-comté, Besançon, FRANCE

Companies are using satisfaction surveys more and more to improve their sales force. With the dev... more Companies are using satisfaction surveys more and more to improve their sales force. With the development of new technologies, the piloting of these surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived. Mass customization is getting to be more prevalent than ever. Current suggestion frameworks such as content-based sifting and collaborative sifting u...

Research paper thumbnail of Enriched Semantic Recommender System For Education

Research paper thumbnail of Enriched semantic recommendation system : application to the e-marketing domain

HAL (Le Centre pour la Communication Scientifique Directe), Apr 25, 2022

Research paper thumbnail of Representation of Knowledge Extracted from the Digital Booklet Data for the Work-Study Program

Research paper thumbnail of Studea: Digital Booklet for Apprenticeship

Research paper thumbnail of Semantic Recommender System Using Machine Learning for Education

Research paper thumbnail of Recommender System Using Unsupervised Machine Learning for Satisfaction Surveys

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; and Theory and Practice in Modern Computing 2019, 2019

Satisfaction surveys are being used more and more by companies to improve their sales force. With... more Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of new technologies the piloting of these satisfaction surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the recommendation system and to make the system partially autonomous on decision-making (to less involving the recommendation engine). Our approach is divided into a) the recommendation process for decision-making, b) unsupervised ML and c) partial "empowerment" for decision-making.

Research paper thumbnail of Organisation of Knowledge from Traces of Human Learning

Proceedings of the International Conference on e-Learning 2019, 2019

Research paper thumbnail of Recommender System: Collaborative Filtering of e-Learning Resources

The significant amount of information available on the web has led to difficulties for the learne... more The significant amount of information available on the web has led to difficulties for the learner to find useful information and relevant resources to carry out their training. The recommender systems have achieved significant success in the area of e-commerce, they still have difficulties in formulating relevant recommendations on e-learning resources because of the different characteristics of learners. Most of the existing recommendation techniques do not take these characteristics into account. This problem can be mitigated by including learner information in the referral process. Currently many recommendation techniques have cold start problems and classification problems. In this paper, we propose an ontology-based collaborative filtering recommendation system for recommending learners' online learning resources based on a decision algorithm (DA). In our approach, ontology is used to model and represent domain knowledge about the learner and learning resources. Our approa...

Research paper thumbnail of Machine Learning 2019: Collaborative filtering combined with machine learning for satisfaction surveys - Baba MBAYE - University of Franche-comté, Besançon, FRANCE

Companies are using satisfaction surveys more and more to improve their sales force. With the dev... more Companies are using satisfaction surveys more and more to improve their sales force. With the development of new technologies, the piloting of these surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived. Mass customization is getting to be more prevalent than ever. Current suggestion frameworks such as content-based sifting and collaborative sifting u...

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