USING RECOMMENDATION SYSTEM TO HELP STUDENTS CHOOSE A CAREER FIELD BASED ON THEIR INTERESTS

Shivendra Saurav, Shubham Kumar Giri, Shivani Sharma, Shiwani ., Prof. Surendra Babu KN

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.


Keywords


study Recommendation Systems to assist users in the retrieval of relevant goods and services, mostly used in e-commerce.

Full Text:

PDF

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




DOI: https://doi.org/10.26483/ijarcs.v11i0.6602

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 International Journal of Advanced Research in Computer Science