Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Recommenders System for Effective ICT Based Learning

Author : Mrs.G Akshaya 1 Rohit Reddy 2 Reddy Supriya 3 Akhileshwar Reddy 4 Vishnu Varma 5

Date of Publication :7th April 2016

Abstract: Recommended Systems for Information & communication technology (ICT-RS) provide personalized services for recommended system. It provides learning objects for teachers and students. User profiling mechanisms are used for recommended system. This paper proposesICT-RSwhich targets to support users in selecting Objects from existing Object Repositories. Automatically constructing their ICT Competence Profiles based on their actions within these ORs.. In technology enhanced learning (Tel) major topic is based on user learning profile but not on student learning profile. So in our proposed system teachers and students have equal priority in selecting a Learning objects

Reference :

    1. J. Bobadilla, F. Ortega, A. Hernando, and J. Alcala, "Improving Collaborative Filtering Recommender System Results And Performance Using Genetic Algorithms," Knowledge-Based Systems, vol. 46, no. 8, pp. 1310-1316, 2011.
    2. J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender Systems Survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.
    3. P. Lops, M.de Gemmis,and G.Semeraro,"Content-Based Recommender 11 Systems: State Of The Art And Trends," Recommender systems handbook,F. Ricci,L.Rokach, B.Shapira and P.B Kantor, eds., US: Springer,pp. 73- 105,2011.
    4. J.B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, "Collaborative Filtering Recommender Systems," The adaptive web, P. Brusilovsky, A. Kobsa, W. Nejdl, eds., Berlin Heidelberg: Springer, pp. 291-324, 2007.
    5. R. Burke, "Hybrid Recommender Systems: Survey And Experiments," User modeling and user-adapted interaction, vol. 12, no. 4, pp. 331-370, 2002.
    6. M. Ferreira-Satler, F.P. Romero, V.H. MenendezDominguez, A. Zapata, and M.E. Prieto, "Fuzzy OntologiesBased User Profiles Applied To Enhance E-Learning Activities," Soft Computing, vol. 16, no. 7, pp. 1129-1141, 2013
    7. L.Marin, A. Moreno, and D. Isern, "Automatic Preference Learning On Numeric And Multi-Valued Categorical Attributes,"Knowledge-Based Systems, vol. 56, pp. 201-215, 2014.
    8. M. de Gemmis, L.Iaquinta, P.Lops, C.Musto, F.Narducci, and G, Semeraro,"Learning Preference Models In Recommender Systems”.
    9. M. Zanker, and M. Jessenitschnig, "Case-Studies On Exploiting Explicit Customer Requirements In Recommender Systems,"User Modeling and User-Adapted Interaction.
    10. L. Marin, D.Isern, A.Moreno, and A.Valls, "On-Line Dynamic Adaptation Of Fuzzy Preferences,"Information Science.
    11. Stylianos Sergis,” Eliciting Teachers' ICT Competence Profiles Based on Usage Patterns within Learning Object Repositories”.
    12. Pasquale Lops,Marcode Gemmis and Giovanni Semeraro,” Content-based Recommender Systems: State of the Art and Trends”.

Recent Article