Abstract
Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover, these algorithms cannot effectively use the learner’s historical sequential patterns of material accessing in recommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach, modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners’ rating and model dynamic and multi-preference of learners in the multidimensional attribute-based CF approach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learner’s real learning preference can be satisfied accurately according to the real-time up dated contextual information.
Similar content being viewed by others
Notes
- Probabilistic latent semantic analysis
References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Bobadilla, J., Serradilla, F., Hernando, A., & MovieLens. (2009). Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems, 22(4), 261–265.
Bobadilla, J., Serradilla, F., & Bernal, J. (2010). A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems, 23(6), 520–528.
Chen, W., Niu, Z., Zhao, X., & Li, Y. (2012). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web. doi:10.1007/s11280-012-0187-z.
Cotter, P., & Smyth, B. (2000). A personalized TV listing service. Communications of the ACM, 43(8), 107–111.
Drachsler, H., Hummel H.G. K., Koper, R., 2007. Recommendations for learners are different: Applying memory-based recommender system technique, Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning and Exchange, 18-26.
Drachsler, H., Hummel, H., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404–423.
Felfernig, A., Friedrich G., Schmidt-Thieme, L., (2007). Introduction to the IEEE Intelligent Systems Special Issue: Recommender Systems, 22(3) 18–21
García, E., Romero, C., Ventura, S., & Castroa, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77–88.
García, E., Romero, C., Ventura, S., & Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1–2), 99–132.
Hammouda K., Kamel M., (2006). Collaborative document clustering, In Proceedings of the Sixth SIAM International Conference on Data Mining (SDM06), 453–463
Hofmann T., (2004). Latent semantic models for collaborative filtering, ACM Transactions on Information Systems, 22(1), 89–115.
Jakob Nielsen (2006). Participation Inequality: Lurkers vs. Contributors in Internet Communities, http://www.useit.com/alertbox/participation_inequality.html
Kay, J. (2008). Lifelong learner modeling for lifelong personalized pervasive learning. IEEE transaction on learning technology, 1(4), 215–228.
Khribi, M. K., Jemni, M., & Nasraoui, O. (2009). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology and Society, 12(4), 30–42.
Liang, G., Weining, K., Junzhou, L., (2006). Courseware recommendation in e-learning system. ICWL’06 Proceedings of the 5th international conference on Advances in Web Based Learning, 10–24.
Lops P., Gemmis M., Semeraro G., (2011). Content-based Recommender Systems: State of the Art and Trends. Recommender Systems Handbook, 73–105.
Marlin B., (2004). Modeling user rating profiles for collaborative filtering. Advances in Neural Information Processing Systems, volume 16.
Milicevic, A. K., Nanopoulos, A., & Ivanovic, M. (2010). Social tagging in recommender systems: A survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 33(3), 187–209.
Mobasher, B. (2007). Data Mining for Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization, 1–46.
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., (2001). Effective personalization based on association rule discovery from web usage data. In Proceedings of the third ACM Workshop on Web Information and Data Management, 9–15.
Pei J., Han J., Mortazavi-Asl B., Pinto H., 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the IEEE 17th International conference on Data Engineering, 215–226.
Romero, C., Ventura, S., Zafra, A., & Bra, P. (2009). Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers and Education, 53, 828–840.
Romero, C., Ventura, S., & Garca, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computer and Education, 51(1), 368–384.
Sergio, F., Sergio, G., Andrea, T., & Ester, Z. (2005). Mining user preferences, page content and usage to personalize website navigation. World Wide Web. Internet Web Inf Syst, 8, 317–345.
Shih, Y. Y., & Liu, D. R. (2008). Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications, 35(1–2), 350–360.
Soonthornphisaj, N., Rojsattarat, E., & Yim-ngam, S. (2006). Smart E-learning using recommender system. In Computational intelligence, 4114, 518–523.
Stevens M., Sotirov A., Appelbaum J., Lenstra A., Molnar D., Osvik A.D., Weger, B., (2009). Short chosen-prefix collisions for MD5 and the creation of a rogue CA certificate. Proceedings of the 29th Annual International Cryptology Conference on Advances in Cryptology, 55–69.
Tan, H., Guo, J., & Li, Y. (2008). E-Learning Recommendation System, international Conference on Computer Science and Software. Engineering, 5, 430–433.
Tao F., Murtagh F., Farid M. (2003). Weighted association rule mining using in Weighted Support and Significance Framework. In Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining, Washington DC, USA. 661–666.
Wang, Y.-H., & Liao, H.-C. (2011). Data mining for adaptive learning in a TESL-based e-learning system. Expert Systems with Applications, 38(6), 6480–6485.
Yu, L., Li, Q., Xie, H., Cai, Y. (2011). Exploring folksonomy and cooking procedures to boost cooking recipe recommendation. In proceedings of APWeb, 119–130.
Yu, H., & Hayato, Y. (2006). Generalized sequential pattern mining with item intervals. Journal of computers, 1(3), 51–60.
Zaki, M. J. (2001). An efficient algorithm for mining frequent sequences. Machine Learning, 40, 31–60.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Salehi, M., Nakhai Kamalabadi, I. & Ghaznavi Ghoushchi, M.B. Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering. Educ Inf Technol 19, 713–735 (2014). https://doi.org/10.1007/s10639-012-9245-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-012-9245-5