USING RECOMMENDATION SYSTEM TO HELP STUDENTS CHOOSE A CAREER FIELD BASED ON THEIR INTERESTS
Main Article Content
Abstract
Several         researchers         study Recommendation Systems to assist users in the retrieval of relevant goods and services, mostly used in e-commerce.
Several         researchers         study Recommendation Systems to assist users in the retrieval of relevant goods and services, mostly used in e-commerce.
However, there is limited information of the impact of Recommendation Systems in other domains like education. Thus, the objective of this study is to summarize the current knowledge that is available as regards Recommendation Systems that have been employed within the education domain to support educational practices.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
Robin Burke, Alexander Felfernig, Mehmet
H. Göker, Recommender Systems: An Overview, http://josquin.cs.depaul.edu/~rburke/pubs/burk e-etal-aimag11a.pdf
Recommender Systems the start of marketing personalization -
https://datasciencetips.com/recommender- systems-the-start-of-marketing- personalization/
Francesco Ricci and Lior Rokach and Bracha Shapira, Introduction to Recommender
Systems Handbook, Recommender Systems Handbook, Springer, 2011, pp. 1-35
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh Zadeh WTF:The who-to-follow system at Twitter, Proceedings of the 22nd international conference on World Wide Web
John S. Breese; David Heckerman & Carl Kadie (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI'98). arXiv:1301.7363.
D.H. Wang, Y.C. Liang, D.Xu, X.Y. Feng,
R.C. Guan(2018), "A content-based recommender system for computer science publications", Knowledge-Based Systems, 157: 1-9
Lakiotaki, K.; Matsatsinis; Tsoukias, A (March 2011). "Multicriteria User Modeling in Recommender Systems". IEEE Intelligent Systems. 26 (2): 64–76. CiteSeerX 10.1.1.476.6726. doi:10.1109/mis.2011.33.
Bouneffouf, Djallel (2013), DRARS, A Dynamic Risk-Aware Recommender System (Ph.D.), Institut National des Télécommunications
Flask Installation - https://flask.palletsprojects.com/en/1.1.x/instal lation/#install-flask
Flask Quickstart (Get started with a Simple Flask Project) - https://flask.palletsprojects.com/en/1.1.x/quick start/#a-minimal-application