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Issue No.04 - Oct.-Dec. (2013 vol.6)
pp: 350-363
Mojtaba Salehi , K. N. Toosi University of Technology, Tehran
Isa Nakhai Kamalabadi , Tarbiat Modares University, Tehran
Mohammad B. Ghaznavi Ghoushchi , Shahed University, Tehran
Personalized recommendations are used to support the activities of learners in personal learning environments and this technology can deliver suitable learning resources to learners. This paper models the dynamic multipreferences of learners using the multidimensional attributes of resource and learner ratings by using data mining technology to alleviate sparsity and cold-start problems and increase the diversity of the recommendation list. The presented approach has two main modules: an explicit attribute-based recommender and an implicit attribute-based recommender. In the first module, a learner preference tree (LPT) is introduced to model the interests of learners based on the explicit multidimensional attributes of resources and historical ratings of accessed resources. Then, recommendations are generated by nearest neighborhood collaborative filtering (NNCF). In the second module, the weights of implicit or latent attributes of resources for learners are considered as chromosomes in a genetic algorithm (GA), and then this algorithm optimizes the weights according to historical ratings. Then, recommendations are generated by NNCF using the optimized weight vectors of implicit attributes. The experimental results show that the proposed method outperforms current algorithms on accuracy measures and can alleviate cold-start and sparsity problems and also generate a more diverse recommendation list.
Recommender systems, Electronic learning, Collaboration, Genetic algorithms, Data mining,implicit attribute, Collaborative filtering, learning environment, sparsity, personalized recommender, genetic algorithm, explicit attribute
Mojtaba Salehi, Isa Nakhai Kamalabadi, Mohammad B. Ghaznavi Ghoushchi, "An Effective Recommendation Framework for Personal Learning Environments Using a Learner Preference Tree and a GA", IEEE Transactions on Learning Technologies, vol.6, no. 4, pp. 350-363, Oct.-Dec. 2013, doi:10.1109/TLT.2013.28
[1] W. Chen, Z. Niu, X. Zhao, and Y. Li, "A Hybrid Recommendation Algorithm Adapted in E-Learning Environments," World Wide Web, Sept. 2012, doi:10.1007/s11280-012-0187-z.
[2] B. Mobasher, "Data Mining for Personalization," The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl, eds., pp. 1-46, Springer, 2007.
[3] A.I. Schein, A. Popescul, and L.H. Ungar, "Methods and Metrics for Cold-Start Recommendations," Proc. 25th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 253-260, 2002.
[4] S. McNee, J. Riedl, and J.A. Konstan, "Being Accurate Is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems," Proc. ACM SIGCHI Extended Abstracts on Human Factors in Computing Systems (CHI EA '06), pp. 1097-1101, 2006.
[5] R. Burke, "Hybrid: Recommender Systems: Survey and Experiments," J. User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.
[6] J. kay, "Lifelong Learner Modeling for Lifelong Personalized Pervasive Learning," IEEE Trans. Learning Technology, vol. 1, no. 4, pp. 215-228, Oct. 2008.
[7] A. Tzikopoulos, N. Manouselis, and R. Vuorikari, "An Overview of Learning Object Repositories," Learning Objects for Instruction: Design and Evaluation, P. Northrup, ed., pp. 29-55, Idea Group, 2007.
[8] V. Kumar, J. Nesbit, and K. Han, "Rating Learning Object Quality with Distributed Bayesian Belief Networks: The Why and the How," Proc. Fifth IEEE Int'l Conf. Advanced Learning Technologies (ICALT '05), pp. 685-687, 2005.
[9] N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, and R. Koper, "Recommender Systems in Technology Enhanced Learning," Recommender Systems Handbook, P.B. Kantor, F. Ricci, L. Rokach, and B. Shapira, eds., pp. 387-415, Springer, 2011.
[10] P. Lops, M. de Gemmis, and G. Semeraro, "Content-Based Recommender Systems: State of the Art and Trends," Recommender Systems Handbook, pp. 73-105, Springer, 2011.
[11] P.-C. Chang and C.-Y. Lai, "A Hybrid System Knowledge-Based Systems, Combining Self-Organizing Maps with Case-Based Reasoning in Wholesaler's New-Release Book Forecasting," Expert Systems with Applications, vol. 29, no. 1, pp. 183-192, 2005.
[12] Y. Blanco-Fernandez et al., "A Flexible Semantic Inference Methodology to Reason About User Preferences in Knowledge-Based Recommender Systems," Knowledge-Based Systems, vol. 21, no. 4, pp. 305-320, 2008.
[13] G. Adomavicius, N. Manouselis, and Y. Kwon, "Multi-Criteria Recommender Systems," Recommender Systems Handbook, pp. 769-803, Springer, 2011.
[14] 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 and Soc., vol. 12, no. 4, pp. 30-42, 2009.
[15] J. Bobadilla, F. Serradilla, and J. Bernal, "A New Collaborative Filtering Metric That Improves the Behavior of Recommender Systems," Knowledge Based System, vol. 23, no. 6, pp. 520-528, 2010.
[16] E. García, C. Romero, S. Ventura, and C. Castro, "An Architecture for Making Recommendations to Courseware Authors Using Association Rule Mining and Collaborative Filtering," User Modeling and User-Adapted Interaction, vol. 19, no. 1, pp. 99-132, 2009.
[17] E. García, C. Romero, S. Ventura, and C.D. Castroa, "A Collaborative Educational Association Rule Mining Tool," Internet and Higher Education, vol. 14, no. 2, pp. 77-88, 2011.
[18] A. Walker, M. Recker, K. Lawless, and D. Wiley, "Collaborative Information Filtering: A Review and an Educational Application," Int'l J. Artificial Intelligence and Education, vol. 14, pp. 1-26, 2004.
[19] D. Lemire, H. Boley, S. McGrath, and M. Ball, "Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation," Int'l J. Interactive Technology and Smart Education, vol. 2, no. 3, pp. 179-188, 2005.
[20] S. Rafaeli, Y. Dan-Gur, and M. Barak, "Social Recommender Systems: Recommendations in Support of E-Learning," Int'l J. Distance Education Technologies, vol. 3, no. 2, pp. 29-45, 2005.
[21] N. Manouselis, R. Vuorikari, and F. Van Assche, "Simulated Analysis of MAUT Collaborative Filtering for Learning Object Recommendation," Proc. First Workshop Social Information Retrieval for Technology Enhanced Learning, pp. 27-35, 2007.
[22] L. Yu, Q. Li, H. Xie, and Y. Cai, "Exploring Folksonomy and Cooking Procedures to Boost Cooking Recipe Recommendation," Proc. 13th Asia-Pacific Web Conf. Web Technologies and Applications (APWeb '11), pp. 119-130, 2011.
[23] C. Romero, S. Ventura, A. Zafra, and P. de Bra, "Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems," Computers and Education, vol. 53, no. 3, pp. 828-840, 2009.
[24] M.S. Chen, J. Han, and P.S. Yu, "Data Mining: An Overview from a Database Perspective," IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 866-883, Dec. 1996.
[25] Y. Li, Z. Niu, W. Chen, and W. Zhang, "Combining Collaborative Filtering and Sequential Pattern Mining for Recommendation in E-Learning Environment," Proc. 10th Int'l Conf. Advances in Web-Based Learning, pp. 305-313, 2011.
[26] R.J. Nadolski, B. Van den Berg, A.J. Berlanga, H. Drachsler, H.G. Hummel, R. Koper, and P.B. Sloep, "Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies," J. Artificial Societies and Social Simulation, vol. 12, no. 1, 2009.
[27] T.Y. Tang and G.I. McCalla, "Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment," Int'l J. E-Learning, vol. 4, no. 1, pp. 105-129, 2005.
[28] K.H. Tsai, T.K. Chiu, M.C. Lee, and T.I. Wang, "A Learning Objects Recommendation Model Based on the Preference and Ontological Approaches," Proc. IEEE Sixth Int'l Conf. Advanced Learning Technologies, pp. 36-40, 2006.
[29] R. Gluga, J. Kay, and T. Lever, "Modeling Long Term Learning of Generic Skills," Intelligent Tutoring Systems, vol. 1, pp. 85-94, 2010.
[30] K.I. Ghauth and N.A. Abdullah, "Learning Materials Recommendation Using Good Learners' Ratings and Content-Based Filtering," Educational Technology Research and Development, vol. 58, no. 6, pp. 711-727, 2010.
[31] M. Salehi, I. Nakhai Kamalabadi, and M.B. Ghaznavi Ghoushchi, "Personalized Recommendation of Learning Material Using Sequential Pattern Mining and Attribute Based Collaborative Filtering," Education and Information Technologies, pp. 1-23, Dec. 2012, doi:10.1007/s10639-012-9245-5.
[32] M. Salehi and I. Nakhai Kamalabadi, "Hybrid Recommendation Approach for Learning Material Based on Sequential Pattern of the Accessed Material and the Learner's Preference Tree," Knowledge-Based Systems, vol. 48, pp. 57-69, 2013.
[33] M. Salehi, M. Pourzaferani, and S.A. Razavi, "Hybrid Attribute-Based Recommender System for Learning Material Using Genetic Algorithm and a Multidimensional Information Model," Egyptian Informatics J., vol. 14, no. 1, pp. 67-78, 2013.
[34] L. Chen and P. Pu, "Survey of Preference Elicitation Methods," Proc. First Workshop Social Information Retrieval for Technology-Enhanced Learning and Exchange,, 2004.
[35] J. Zhong and X. Li, "Unified Collaborative Filtering Model Based on Combination of Latent Features," Expert Systems with Applications, vol. 37, no. 8, pp. 5666-5672, 2010.
[36] G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
[37] K. Verbert, N. Manouselis, H. Drachsler, and E. Duval, "Dataset-Driven Research to Support Learning and Knowledge Analytics," Educational Technology and Soc., vol. 15, no. 3, pp. 133-148, 2012.
[38] Y.Y. Shih and D.R. Liu, "Product Recommendation Approaches: Collaborative Filtering via Customer Lifetime Value and Customer Demands," Expert Systems with Applications, vol. 35, nos. 1/2, pp. 350-360, 2008.
[39] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
[40] H. Ma, I. King, and M.R. Lyu, "Effective Missing Data Prediction for Collaborative Filtering," Proc. 30th Int'l ACM SIGIR Conf. Information Retrieval, pp. 39-46, 2007.
[41] P. Baudisch, "Joining Collaborative and Content-Based Filtering," Proc. Conf. Human Factors in Computing Systems, 1999.
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