2008 Eighth IEEE International Conference on Advanced Learning Technologies 2008
DOI: 10.1109/icalt.2008.198
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Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

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Cited by 244 publications
(178 citation statements)
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“…A collaborative filtering system can recommend content to a user by learning from similar users, or by detecting groups of similar items (Khribi, Jemni and Nasraoui 2009). …”
Section: Collaborative Filtering Systemmentioning
confidence: 99%
“…A collaborative filtering system can recommend content to a user by learning from similar users, or by detecting groups of similar items (Khribi, Jemni and Nasraoui 2009). …”
Section: Collaborative Filtering Systemmentioning
confidence: 99%
“…Researchers and developers of e-learning have begun to apply information retrieval techniques with technologies for recommendation, especially collaborative filtering [1], or web mining [2], for recommending educational content. A recent review of these applications can be seen in [3].…”
Section: Related Workmentioning
confidence: 99%
“…Concretely, (García et al, 2009) uses association rule mining to discover interesting information through student performance data in the form of IF-THEN rules, then generating the recommendations based on those rules; (Bobadilla et al, 2009) proposed an equation for collaborative filtering which incorporated the test score from the learners into the item prediction function; (Ge et al, 2006) combined the content-based filtering and collaborative filtering to personalize the recommendations for a courseware selection module; (Soonthornphisaj et al, 2006) applied collaborative filtering to predict the most suitable documents for the learners; while (Khribi et al, 2008) employed web mining techniques with content-based and collaborative filtering to compute the relevant links for recommending to the learners.…”
Section: Related Workmentioning
confidence: 99%