2018 International Symposium on Computers in Education (SIIE) 2018
DOI: 10.1109/siie.2018.8586681
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Collaborative Filtering based Course Recommender using OWA operators

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Cited by 7 publications
(10 citation statements)
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“…From the analysis, it can then be observed that RSs take into account user preferences when making suggestions based on recommendations from similar users, while [21,30,35,45,53] make recommendations based on learning style, and [24,31,46,50,61,64] based on diagnosis/student progress and the knowledge group. Likewise, another element they take into account are user skills and/or competences [40,42,56,69] and competences related to work associated with their profile within Internet job search portals.…”
Section: Discussionmentioning
confidence: 99%
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“…From the analysis, it can then be observed that RSs take into account user preferences when making suggestions based on recommendations from similar users, while [21,30,35,45,53] make recommendations based on learning style, and [24,31,46,50,61,64] based on diagnosis/student progress and the knowledge group. Likewise, another element they take into account are user skills and/or competences [40,42,56,69] and competences related to work associated with their profile within Internet job search portals.…”
Section: Discussionmentioning
confidence: 99%
“…In terms of RSs that use the collaborative approach, they are thought to evidence the cold-start problem, with works [36,41,56] suggesting a greater volume of data to improve performance, and [22,48] adding more parameters to the user profile, such as learning styles or reading tastes-in general, it is suggested that this be combined with other approaches in order to improve performance. Ref.…”
Section: Discussionmentioning
confidence: 99%
“…The main challenge in CFRS is to overcome the cold-start problem. Some research studies overcome this by taking weighted average of most recent grades of the courses taken by the student [16] and using K-Modes algorithm to partition users based on demographic profiles and assign cluster average rating to the users in the same cluster [132]. Table 2 illustrates the summary of CFRS-based course RS systems identified in the systematic review in terms of drawbacks or comments.…”
Section: Collaborative Filtering-based Recommendation System (Cfrs)mentioning
confidence: 99%
“…Bozyigit et al [16] Cold-start problem is overcome by taking Weighted Averaging of most recent grades of the courses taken by the students Bakhshinategh et al [134] The results showed that better performance can be obtained by selecting the number of users in the neighborhood for K-nearest neighbor between 10 to 15 students Huang et al [133] Novel cross-user-domain CF by using course-score distribution of the most similar senior students, scalability issues is addressed by selecting the top t optional courses with the highest predicted scores. Extensive testing has been conducted.…”
Section: Collaborative Filtering-based Recommendation System (Cfrs)mentioning
confidence: 99%
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