A learner&amp;#x2019;s role-based multi dimensional collaborative recommendation (LRMDCR) for group learning support

Wan, Xin; Ninomiya, Toshie; Okamoto, Toshio

doi:10.1109/ijcnn.2008.4634360

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Collaborative method can be classified into three categories according to their algorithmic techniques which are memory based, model based and hybrid [9].  Content-based Filtering Method: Content-based method or item-to-item correlation recommends items based on a comparison between items and a user profile [10]. It recommends items that are similar to the ones the user preferred in the past based on textual information of an item.…”

Section: Recommender Systemmentioning

confidence: 99%

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Collaborative Recommender System A Review

Shuib¹

2015

Third International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2015

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Recommender system is widely used in various domains to assist users in finding items that relate to their needs. The collaborate filtering method is one of the popular methods in recommender systems. By using collaborative filtering method, users can make an assessment of the item based on other user experience. This method is very beneficial in education domain to assist student in getting a learning object that can help them in learning process. However, there is no review focusing on the use of the collaborative recommender system in education. Hence, the objective of this study is to review the state of the art of collaborative recommender system in education. This study reviewed twenty six articles based on recommender system components such as input, method and the output of the system. The overview and the applications of collaborative recommender system in education then discussed.

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Section: Recommender Systemmentioning

confidence: 99%

“…This is due to the need in identifying user level of education. Example of User Profile attributes is academic result, academic level, course, learning material and activities [3,4,5,6,7,8,9] Examples of User Preference attributes are by user searching history, such as recommended argument, basic search and keyword search [10,11].…”

Section: A Inputmentioning

confidence: 99%