An Adaptive Intelligent Recommendation Scheme for Smart Learning Contents Management Systems
This study aims to provide personalized contents recommendation services depending on a learner’s learning stage and learning level in the learning management system using open courses. The intelligent recommendation system proposed in this study selects similar neighboring groups by performing user-based collaborative filtering process and recommends phased learning contents by using prior knowledge information between contents and considering the relevance and levels of learning contents. The proposed learning contents recommendation is applied flexibly according to a user’s learning situation and situation-specific contents recommendation link is created by performing the intelligent learning process of recommendation system. This service allows a variety of industrial classification learners using open course to effectively choose more accurate curriculum.
KeywordsPersonalization Recommendation system Collaborative filtering E-learning Learner’s preference
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0014394).
- 1.Cho D-E, Kim S-J, Kwak Y (2011) A study of personalized contents recommendation method based on user preference learning. J Korean Inst Inf Technol 9(9):229–235Google Scholar
- 2.Schafer et al (1999) Recommender system in E-Commerce. In: Proceedings of the ACM E-Commerce 1999 conference Google Scholar
- 3.Arazy O, Kumar N, Shapira B (2010) A theory-driven design framework for social recommender systems. J Assoc Inf Syst 11(9):455–490Google Scholar
- 7.Kang Y-J, Sun C-Y, Park K-S (2010) A Study of IPTV-VOD program recommendation system using hybrid filtering. J Institute Electron Eng Korea 47(4):9–19 Google Scholar
- 8.Inay Ha, Song G-S, Kim H-N et al (2009) Collaborative recommendation of online video lectures in e-learning system. J Korean Soc Comput Inf 9(14):87–94Google Scholar
- 9.Herlocker JJ, Konstan A, Borschers R et al (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22th ACM SIGIR conference on research and development in information retrieval, pp 230–237Google Scholar
- 10.Sarwar B, Karypis G, Konstan J et al (2000) Item-based collaborative filtering recommendation algorithm. WWW10, pp 285–295 Google Scholar