Recommender Systems for E-Learning: Towards Non-Intrusive Web Mining
- Osmar R. Zaiane, University of Alberta (Database)
Recommender systems are software agents that recommend options to users. They are becoming very popular in e-commerce applications to recommend the online purchase of some products. These agents can be very useful in an e-learning environment to recommend actions, resources or simply links to follow. However, most approaches to develop these intelligent agents are based on data explicitly collected from users to build profiles such as rankings, opinions and the like. This can be considered intrusive and a distraction by online learners. In this chapter we discuss methods to build recommender systems for e-learning that are non-intrusive and true to the choices of users.
Citation
O. Zaiane. "Recommender Systems for E-Learning: Towards Non-Intrusive Web Mining". Data Mining in E-Learning, Advances in Management Information - Data Mining in E-Learning, WIT Press, (ed: C. Romero and S. Ventura), 4, pp 79-96, October 2006.| Keywords: | |
| Category: | In Book |
| Web Links: | Webdocs |
BibTeX
@inbook{Zaiane:06,
author = {Osmar R. Zaiane},
title = {Recommender Systems for E-Learning: Towards Non-Intrusive Web
Mining},
Booktitle = {Advances in Management Information - Data Mining in E-Learning},
Publisher = {WIT Press},
Editor = {C. Romero and S. Ventura},
Volume = "4",
Chapter = "5",
Pages = {79-96},
year = 2006,
}Last Updated: February 04, 2020Submitted by Sabina P
