A Comprehensive Survey of Neighborhood-Based Recommendation Methods used in E-Learning Recommender Systems

Authors

  • Saul Nicholas J Department of Computer Science and Applications, SCSVMV University, Enathur, Kanchipuram, Tamil Nadu, India
  • Sagayaraj Francis F Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

DOI:

https://doi.org/10.26438/ijcse/v7i1.443450

Keywords:

E-learning, personalized learning, learning styles, recommender systems, neighborhood-based methods

Abstract

This paper presents the essentials of the background, available literature and technologies presently available in e-leaning specifically recommender systems and its range of applications, different techniques used for the general recommender systems, e-learning recommender systems and the specific neighborhood-based recommender methods used. A comprehensive survey has been carried out to elucidate the types of neighborhood-based recommendation methods used in e-learning recommender systems. The paper highlights these methods with an comparative analysis of the recommendation methods.

References

[1] Schwartz, B, “The Paradox of Choice”, ECCO, New York, 2004.

[2] M. Pazzani and D. Billsus, “Content-based recommendation systems, TheAdaptiveWeb – Springer, pp. 325-341, Heidelberg, Germany, 2007.

[3] M. Deshpande and G. Karypis, “Item-based top-N recommendation algorithms”, ACM Transactions on Information Systems, ISSN: 1046-8188, Volume: 22, pp. 143-177, 2004.

[4] M. Nilashi, O.B. Ibrahim and N. Ithnin, “Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system, Knowledge-Based Systems, ISSN Knowledge-Based Systems, ISSN : 3910-1211, Volume: 60, Issue: 3, pp.82-101, 2014.

[5] R. Burke, “Hybrid recommender systems: survey and experiments”, International Journal of User Model and User Adaption, ISSN: 2124-3765, Volume: 12, Issue: 4, pp.331-370, 2002.

[6] S. Middleton, D. Roure, N. Shadbolt, “Ontology-based recommender systems”, Handbook on Ontologies, Springer Publication, Berlin, 2009.

[7] W. Nejldon and R. Burke, “Hybrid recommender systems: survey and experiments for E-Learning”, International Journal of User Model and User Adaption, ISSN: 2124-3765, Volume: 14, Issue: 2, pp.431-470, 2004.

[8] A. Bellogin, I. Cantador, F. Diez, P. Castells, E. Chavarriaga, “An empirical comparison of social, collaborative filtering, and hybrid recommenders”, ACM Transactions on Intelligent Systems and Technology ISSN: 0318-4908,Volume: 4, Issue: 4, pp.1-29, 2014.

[9] M.M. Recker, D.A. Wiley, “A Non-authoritative educational metadata ontology for filtering and recommending learning objects”, Interactivelearningenvironments”, ISSN: 4231-0376, Volume: 9, Issue: 3, pp.255-271, 2001.

[10] M.M. Recker, A. Walker and D. Wiley, “An interface for collaborative filtering of educational resources”, International Conference on Artificial Intelligence, Las Vegas, U.S.A, pp. 26-29, 2000.

[11] M.M Recker, A. Walker and K. Lawless, “What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education”, Journal of InstructionalScience, ISSN: 6542- 3120, Volume: 31, Issue: 4, pp.299–316, 2003.

[12] A. Walker, M. Recker, K. Lawles and D. Wiley, “Collaborative information filtering: A review and an educational application”, International Journal of Artificial Intelligence in Education, ISSN: 1560-4306, Volume: 14, Issue: 1, pp. 3–28, 2004.

[13] Lemire, “Scale and Translation Invariant Collaborative Filtering Systems”, Journal of Information Retrieval, ISSN: 1386-4564, Volume: 8, Issue: 1, pp.129–150, 2005.

[14] J. Fiaidhi, “RecoSearch: A Model for Collaboratively Filtering Java Learning Objects”, International Journal of Instructional Technology and Distance Learning,ISSN 1550-6908,Volume: 1, Issue: 7, pp.35–50, 2004.

[15] S. Rafaeli, M. Barak, Y.Dan-Gur and E.Toch, “QSIA - A Web-based environment for learning, assessing and knowledge sharing in communities”, ComputersandEducation, Volume: 43, Issue: 3, pp.273–289, 2004.

[16] S. Rafaeli, Y. Dan-Gur and M. Bara, “Social Recommender Systems: Recommendations in Support of E-Learning”, International Journal of Distance Education Technologies, ISSN: 153-3100, Volume: 3, Issue: 3, pp.29–45, 2005.

[17] H. Avancini and U. Straccia, “User recommendation for collaborative and personalised digital archives”, International Journal of Web Based Communities, ISSN: 1539-3100, Volume: 1, Issue: 2, pp.163-175, 2005.

[18] J. Dron, R. Mitchell, C. Boyne and P. Siviter, “CoFIND: steps towards a self-organising learning environment”, Proceedings of the World Conference on the WWW and Internet, Texas, USA, pp. 146-151,2000.

[19] N. Manouselis, R. Vuorikari and F. Van Assche, “Simulated Analysis of MAUT Collaborative Filtering for Learning Object Recommendation”, Proceedings of the Workshop on Social Information Retrieval in Technology Enhanced Learning, Crete, Greece, 2007.

[20] N. Manouselis and C. Costopoulou, “Experimental Analysis of Design Choices in Multi-Attribute Utility Collaborative Filtering”, International Journal of Pattern Recognition and Artificial Intelligence, ISSN: 5498-487, Volume:21, Issue:2, pp.311–331, 2007.

[21] L. Shen, L and R. Shen, “Learning content recommendation service based-on simple sequencing specification”, Lecturenotes in computer science, pp. 363-370, New Jersey, U.S.A, 2004.

[22] Y.M. Huang, T.C. Huang, K.T. Wang and W.Y. Hwang, “A Markov-based Recommendation Model for Exploring the Transfer of Learning on the Web”, Educational Technology & Society, ISSN: 5678-612, Volume: 12, Issue: 2, pp.144–162, 2009.

[23] T. Tang and G. McCalla, “Smart Recommendation for an Evolving E-Learning System”, Proceedings of the Workshop on Technologies for Electronic Documents for Supporting Learning, Tokyo, Japan, 2003.

[24] P. Totterdell and E. Boyle, “The evaluation of adaptive systems”, AdaptiveUserInterfaces”, first edition, Mcgraw Hill, Wahington,1990.

[25] J. Janssen, C. Tattersall, W.Waterink, B. Van den Berg, R. Van Es and C. Bolmanl, “Self-organising navigational support in lifelong learning: how predecessors can lead the way”, Computers & Education, ISSN: 7865-432, Volume: 49, pp. 781–793, 2005.

[26] R.J. Nadolski, B.Van den Berg, A. Berlanga, H. Drachsler, H. Hummel, R. Koper and P. Sloep, “Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies”, Journal of Artificial Societies and Social Simulation, ISSN: 1460-7425, Volume: 12, Issue: 14, 2009.

[27] H.G.K. Hummel, B. Van den Berg, A.J. Berlanga, H. Drachsler, J. Janssen, R.J. Nadolski, and E.J.R. Koper, “Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities”, International Journal of Learning Technology ISSN: 1741-8119, Volume: 3, Issue:2, pp.152–168 ,2007.

[28] R. Koper , “Increasing Learner Retention in a Simulated learning network using Indirect So-cial Interaction”, Journal of Artificial Societies and Social Simulation, ISSN: 1460-7425, Volume: 8, Issue: 2, 2005.

[29] H. Drachsler, H.G.K. Hummel, B. Van den Berg, J. Eshuis, A. Berlanga, R. Nadolski, W. Waterink, N. Boers and R. Koper, “ Effects of the ISIS Recommender System for navigation support in self-organized learning networks”, Journal of Educational Technology and Society, ISSN: 1246-0730, Volume: 12, pp.122-135. 2009.

[30] H. Drachsler, D. Pecceu, T. Arts, E. Hutten, L. Rutledge, P. Van Rosmalen, H.G.K. Hummel and R. Koper, “ReMashed Recommendations for Mash-Up Personal Learning Environments”, Proceedings of the 4th European Conference on Technology Enhanced Learning, Germany, Berlin, 2009.

[31] M. Van Setten, “Supporting people in finding information: hybrid recommender systems and goal-based structuring”, TelematicaInstituutFundamentalResearch, Enschede, The Netherlands, 2005.

[32] K.H. Tsai, T.K. Chiu T.K., M.C. Lee and T.I. Wang, “A learning objects recommendation model based on the preference and ontological approaches”, Proceedingsof6th International Conference on Advanced Learning Technologies, Seoul, South Korea, 2006.

[33] G. Koutrika, R. Ikeda, B. Bercovitz and H. Garcia Molina, “Flexible Recommendations over Rich Data”, Proceedings of thesecond ACM International Conference on Recommender Systems, Lausanne, Switzerland, 2008.

[34] G. Koutrika, B. Bercovitz, F. Kaliszan, H. Liou and H. Garcia-Molina, “CourseRank: A Closed-Community Social System Through the Magnifying Glas”, Proceedings of thethird International AAAI Conference on Weblogs and Social Media, San Jose, California, 2009.

[35] M.K. Khribi, M. Jemni and O. Nasraoui, “Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval”, Educational Technology & Society, ISSN: 7498-1487, Volume: 12, Issue: 4, pp. 30–42, 2009.

[36] M. Gomez Albarran and G. Jimenez Diaz, “Recommendation and Students’ Authoring in Repositories of Learning Objects: A Case-Based Reasoning Approach”, International Journal of Emerging Technologies in Learning, ISSN: 2321-432, Volume: 4, Issue: 1, pp. 35-4-, 2009.

[37] O.C. Santos, “A recommender system to provide adaptive and inclusive standard-based support along the eLearning life cycle”, Proceedings of the 2008 ACM conference on Recommender systems, San Jose, U.S.A., pp. 319-322, 2008.

[38] R. Klamma, M. Spaniol and Y. Cao, “Community Aware Content Adaptation for Mobile Technology Enhanced Learning”, Innovative Approaches for Learning and Knowledge Sharing, pp. 227-241, 2006.

[39] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon and J.Riedl, “GroupLens: applying collaborative filtering to usenet news” Communications of the ACM, ISSN: 0004-5411, Volume: 40, Issue: 3, pp. 77–87 ,1997.

[40] M. Deshpande and G. Karypis, “Item-based top-N recommendation algorithms”, ACM Transaction on Information Systems, ISSN: 0004-1145, Volume: 22, Issue: 1, pp.143-177,2004.

[41] http://www.last.fm.

[42] J.S. Breese, D. Heckerman and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering”, Proceedings of the fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52. San Franciscoo, U.S.A, 1998.

[43] D. Billsus and M.J. Pazzaniand, “Learning collaborative information filters”, Proceedings of the fifteenth International Conference on Machine Learning, San Francisco, U.S.A, 1998.

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Published

2019-01-31
CITATION
DOI: 10.26438/ijcse/v7i1.443450
Published: 2019-01-31

How to Cite

[1]
J. Saul Nicholas and F. Sagayaraj Francis, “A Comprehensive Survey of Neighborhood-Based Recommendation Methods used in E-Learning Recommender Systems”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 443–450, Jan. 2019.

Issue

Vol. 7 No. 1 (2019): IJCSE January Edition

Section

Survey Article

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