A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier
Personal Learning Environment (PLE) solutions can empower learners to design ICT environments for their activities in different learning contexts. Recommender systems have been used for supporting learners in PLEbased activities. Since, in the current recommendation approaches, multidimensional attr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Iran Telecom Research Center
2012-09-01
|
| Series: | International Journal of Information and Communication Technology Research |
| Subjects: | |
| Online Access: | http://ijict.itrc.ac.ir/article-1-177-en.html |
| _version_ | 1811169305489833984 |
|---|---|
| author | Mojtaba Salehi Isa Nakhai Kamalabadi |
| author_facet | Mojtaba Salehi Isa Nakhai Kamalabadi |
| author_sort | Mojtaba Salehi |
| collection | DOAJ |
| description | Personal Learning Environment (PLE) solutions can empower learners to design ICT environments for their activities in different learning contexts. Recommender systems have been used for supporting learners in PLEbased activities. Since, in the current recommendation approaches, multidimensional attributes of resource and dynamic interests and multi-preference of learners are not fully considered simultaneously, this paper proposes a novel resource recommendation framework in order to personalize learning environments. Learner Tree (LT) is introduced to take into account the multidimensional attributes of resources and learners' rating matrix simultaneously. In addition, a forgetting function also is used to reflect dynamic preference of a learner and a Bayesian classifier is used to predict rate of unrated resources. The main contribution of this paper is proposing a multidimensional data model to consider multi-preference of learner and using naive Bayes classifier to improve the quality of recommendation in the terms of precision, recall and also intra-list similarity. In addition, the proposed approach tries to satisfy the learner’s real learning preference accurately according to the real-time up dated contextual information. |
| first_indexed | 2024-04-10T16:40:12Z |
| format | Article |
| id | doaj.art-9850107c1266487b9f0e170c62d57947 |
| institution | Directory Open Access Journal |
| issn | 2251-6107 2783-4425 |
| language | English |
| last_indexed | 2024-04-10T16:40:12Z |
| publishDate | 2012-09-01 |
| publisher | Iran Telecom Research Center |
| record_format | Article |
| series | International Journal of Information and Communication Technology Research |
| spelling | doaj.art-9850107c1266487b9f0e170c62d579472023-02-08T07:32:18ZengIran Telecom Research CenterInternational Journal of Information and Communication Technology Research2251-61072783-44252012-09-01432131A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes ClassifierMojtaba Salehi0Isa Nakhai Kamalabadi1 Personal Learning Environment (PLE) solutions can empower learners to design ICT environments for their activities in different learning contexts. Recommender systems have been used for supporting learners in PLEbased activities. Since, in the current recommendation approaches, multidimensional attributes of resource and dynamic interests and multi-preference of learners are not fully considered simultaneously, this paper proposes a novel resource recommendation framework in order to personalize learning environments. Learner Tree (LT) is introduced to take into account the multidimensional attributes of resources and learners' rating matrix simultaneously. In addition, a forgetting function also is used to reflect dynamic preference of a learner and a Bayesian classifier is used to predict rate of unrated resources. The main contribution of this paper is proposing a multidimensional data model to consider multi-preference of learner and using naive Bayes classifier to improve the quality of recommendation in the terms of precision, recall and also intra-list similarity. In addition, the proposed approach tries to satisfy the learner’s real learning preference accurately according to the real-time up dated contextual information.http://ijict.itrc.ac.ir/article-1-177-en.htmladaptive recommendationmultidimensional recommendationlearning resourcee-learningcollaborative filtering |
| spellingShingle | Mojtaba Salehi Isa Nakhai Kamalabadi A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier International Journal of Information and Communication Technology Research adaptive recommendation multidimensional recommendation learning resource e-learning collaborative filtering |
| title | A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier |
| title_full | A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier |
| title_fullStr | A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier |
| title_full_unstemmed | A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier |
| title_short | A Multi-Dimensional Recommendation Framework for Learning Material by Naive Bayes Classifier |
| title_sort | multi dimensional recommendation framework for learning material by naive bayes classifier |
| topic | adaptive recommendation multidimensional recommendation learning resource e-learning collaborative filtering |
| url | http://ijict.itrc.ac.ir/article-1-177-en.html |
| work_keys_str_mv | AT mojtabasalehi amultidimensionalrecommendationframeworkforlearningmaterialbynaivebayesclassifier AT isanakhaikamalabadi amultidimensionalrecommendationframeworkforlearningmaterialbynaivebayesclassifier AT mojtabasalehi multidimensionalrecommendationframeworkforlearningmaterialbynaivebayesclassifier AT isanakhaikamalabadi multidimensionalrecommendationframeworkforlearningmaterialbynaivebayesclassifier |