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An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem

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Abstract

An e-learning recommender system (RS) aims to generate personalized recommendations based on learner preferences and goals. The existing RSs in the e-learning domain still exhibit drawbacks due to its inability to consider the learner characteristics in the recommendation process. In this paper, we are dealing with the new user cold-start problem, which is a major drawback in e-learning content RSs. This problem can be mitigated by incorporating additional learner data in the recommendation process. This paper proposes an ontology-based (OB) content recommender system for addressing the new user cold-start problem. In the proposed recommendation model, ontology is used to model the learner and learning objects with their characteristics. Collaborative and content-based filtering techniques are used in the recommendation model to generate the top N recommendations based on learner ratings. Experiments were conducted to evaluate the performance and prediction accuracy of the proposed model in cold-start conditions using the evaluation metrics mean absolute error, precision and recall. The proposed model provides more reliable and personalized recommendations by making use of ontological domain knowledge.

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References

  • Adomavicius, G., & Kwon, Y. (2007). New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, 22(3), 48–55.

    Article  Google Scholar 

  • Aeiad, E., & Meziane, F. (2019). An adaptable and personalised E-learning system applied to computer science programmes design. Education and Information Technologies, 24(2), 1485–1509.

    Article  Google Scholar 

  • Al-Yahya, M., George, R., & Alfaries, A. (2015). Ontologies in e-learning: Review of the literature. International Journal of Software Engineering and its Applications, 9(2), 67–84.

    Google Scholar 

  • Atif, Y., Benlamri, R., & Berri, J. (2003). Learning objects based framework for self-adaptive learning. Education and Information Technologies, 8(4), 345–368.

    Article  Google Scholar 

  • Bahmani, A., Sedigh, S., & Hurson, A. (2012). Ontology-based recommendation algorithms for personalized education. In International Conference on Database and Expert Systems Applications (pp. 111–120). Springer.

  • Bajenaru, L., Borozan, A. M., & Smeureanu, I. (2015). Using ontologies for the e-learning system in healthcare human resources management. InformaticaEconomica, 19(2), 15.

    Google Scholar 

  • Barjasteh, I., Forsati, R., Ross, D., Esfahanian, A. H., & Radha, H. (2016). Cold-start recommendation with provable guarantees: A decoupled approach. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1462–1474.

    Article  Google Scholar 

  • Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 180(22), 4290–4311.

    Article  Google Scholar 

  • Benhamdi, S., Babouri, A., & Chiky, R. (2017). Personalized recommender system for e-learning environment. Education and Information Technologies, 22(4), 1455–1477.

    Article  Google Scholar 

  • Bhaskaran, S., & Santhi, B. (2019). An efficient personalized trust based hybrid recommendation (tbhr) strategy for e-learning system in cloud computing. Cluster Computing, 22(1), 1137–1149.

    Article  Google Scholar 

  • Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.

    Article  Google Scholar 

  • Bouihi, B., & Bahaj, M. (2017). An ontology-based architecture for context recommendation system in E-learning and mobile-learning applications. In 2017 International Conference on Electrical and Information Technologies (ICEIT) (pp. 1–6). IEEE.

  • Bourkoukou, O., & El Bachari, E. (2016). E-learning personalization based on collaborative filtering and learner’s preference. Journal of Engineering Science and Technology, 11(11), 1565–1581.

    Google Scholar 

  • Bourkoukou, O., El Bachari, E., & El Adnani, M. (2017). A recommender model in e-learning environment. Arabian Journal for Science and Engineering, 42(2), 607–617.

    Article  Google Scholar 

  • Buder, J., & Schwind, C. (2012). Learning with personalized recommender systems: A psychological view. Computers in Human Behavior, 28(1), 207–216.

    Article  Google Scholar 

  • Buitrago, M., & Chiappe, A. (2019). Representation of knowledge in digital educational environments: A systematic review of literature. Australasian Journal of Educational Technology, 35(4).

  • Burke, R. (2007). Hybrid web recommender systems. In The adaptive web (pp. 377–408). Springer.

  • Cakula, S., & Sedleniece, M. (2013). Development of a personalized e-learning model using methods of ontology. Procedia Computer Science, 26, 113–120.

    Article  Google Scholar 

  • Chen, H., Cui, X., & Jin, H. (2016). Top-k followee recommendation over microblogging systems by exploiting diverse information sources. Future Generation Computer Systems, 55, 534–543.

    Article  Google Scholar 

  • Chen, W., Niu, Z., Zhao, X., & Li, Y. (2014). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 17(2), 271–284.

    Article  Google Scholar 

  • Ciloglugil, B., & Inceoglu, M. M. (2016). Ontology usage in e-learning systems focusing on metadata modeling of learning objects. In International Conference on New Trends in Education, ICNTE, pp. 80–96.

  • Deschênes, M. (2020). Recommender systems to support learners' agency in a learning context: A systematic review. International Journal of Educational Technology in Higher Education, 17(1), 1–23.

    Article  Google Scholar 

  • Dwivedi, P., & Bharadwaj, K. K. (2013). Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Journal of Educational Technology & Society, 16(4), 201–216.

    Google Scholar 

  • Dwivedi, P., Kant, V., & Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2), 819–836.

    Article  Google Scholar 

  • Essalmi, F., Ayed, L. J. B., Jemni, M., & Graf, S. (2010). A fully personalization strategy of e-learning scenarios. Computers in Human Behavior, 26(4), 581–591.

    Article  Google Scholar 

  • Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.

    Google Scholar 

  • Fraihat, S., & Shambour, Q. (2015). A framework of semantic recommender system for e-learning. Journal of Software, 10(3), 317–330.

    Article  Google Scholar 

  • George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.

    Article  Google Scholar 

  • Graf, S., & Kinshuk, K. (2007). Providing adaptive courses in learning management systems with respect to learning styles. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2576–2583). Association for the Advancement of Computing in Education.

  • Graf, S., Viola, S. R., Leo, T., & Kinshuk. (2007). In-depth analysis of the felder-silverman learning style dimensions. Journal of Research on Technology in Education, 40(1), 79–93.

    Article  Google Scholar 

  • Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–221.

    Article  Google Scholar 

  • Harrathi, M., Touzani, N., & Braham, R. (2017). A hybrid knowlegde-based approach for recommending massive learning activities. In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) (pp. 49–54). IEEE.

  • Holzinger, A., Smolle, J., & Reibnegger, G. (2006). An object-oriented approach to manage e-learning content using learning objects. In Handbook of research on informatics in healthcare and biomedicine (pp. 89–98).

  • IEEE-LTSC. (2010). IEEE P1484.12.1–2002/Cor 1/D14. Draft standard for learning object metadata — corrigendum 1: corrigenda for 1484.12.1 LOM (learning object metadata), IEEE Learning Technology Standards Committee.

  • Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge University Press.

  • Joy, J., Raj, N. S. & Renumol V.G. (2019). An ontology model for content recommendation in personalized learning environment. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (pp. 1–6). ACM.

  • Kamal, A., & Radhakrishnan, S. (2019). Individual learning preferences based on personality traits in an e-learning scenario. Education and Information Technologies, 24(1), 407–435.

    Article  Google Scholar 

  • Karga, S., & Satratzemi, M. (2018). A hybrid recommender system integrated into LAMS for learning designers. Education and Information Technologies, 23(3), 1297–1329.

    Article  Google Scholar 

  • Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2019). A systematic review: Machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4), 2635–2664.

  • Kilani, Y., Alhijawi, B., & Alsarhan, A. (2018). Using artificial intelligence techniques in collaborative filtering recommender systems: Survey. International Journal of Advanced Intelligence Paradigms, 11(3–4), 378–396.

    Article  Google Scholar 

  • Kim, S. C., Sung, K. J., Park, C. S., & Kim, S. K. (2016). Improvement of collaborative filtering using rating normalization. Multimedia Tools and Applications, 75(9), 4957–4968.

    Article  Google Scholar 

  • Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899.

    Article  Google Scholar 

  • Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: A survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571–604.

    Article  Google Scholar 

  • Klašnja-Milićević, A., Ivanović, M., Vesin, B., & Budimac, Z. (2018). Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence, 48(6), 1519–1535.

    Article  Google Scholar 

  • Kolekar, S. V., Pai, R. M., & ManoharaPai, M. M. (2019). Rule based adaptive user interface for adaptive e-learning system. Education and Information Technologies, 24(1), 613–641.

    Article  Google Scholar 

  • Lam, X. N., Vu, T., Le, T. D., & Duong, A. D. (2008). Addressing cold-start problem in recommendation systems. In Proceedings of the 2nd international conference on Ubiquitous information management and communication (pp. 208–211).

  • Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, 41(4), 2065–2073.

    Article  Google Scholar 

  • Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook (pp. 387–415). Springer.

  • Mobasher, B. (2007). Data mining for web personalization. In The adaptive web (pp. 90–135). Springer.

  • Murad, D. F., Heryadi, Y., Isa, S. M., & Budiharto, W. (2020). Personalization of study material based on predicted final grades using multi-criteria user-collaborative filtering recommender system. Education and Information Technologies, 25, 5655–5668.

    Article  Google Scholar 

  • Nafea, S., Maglaras, L. A., Iewe, F., Smith, R., & Janicke, H. (2016). Personalized students’ profile based on ontology and rule-based reasoning. EAI Endorsed Transactions on E-Learning, 3(12), 151720.

    Article  Google Scholar 

  • Nafea, S. M., Siewe, F., & He, Y. (2019). On recommendation of learning objects using felder-silverman learning style model. IEEE Access, 7, 163034–163048.

    Article  Google Scholar 

  • Najafabadi, M. K., & Mahrin, M. N. R. (2016). A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artificial intelligence review, 45(2), 167–201.

    Article  Google Scholar 

  • Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A. H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Systems with Applications, 149, 113248.

    Article  Google Scholar 

  • Ouf, S., Ellatif, M. A., Salama, S. E., & Helmy, Y. (2017). A proposed paradigm for smart learning environment based on semantic web. Computers in Human Behavior, 72, 796–818.

    Article  Google Scholar 

  • Park, S. T., Pennock, D., Madani, O., Good, N., & DeCoste, D. (2006). Naïve filterbots for robust cold-start recommendations. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 699–705).

  • Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp. 325–341). Springer.

  • Premlatha, K. R., & Geetha, T. V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4), 443–465.

    Article  Google Scholar 

  • Pukkhem, N. (2013). Ontology-based semantic approach for learning object recommendation. International Journal on Information Technology, 3(4), 12.

    Google Scholar 

  • Pukkhem, N. (2014). LORecommendNet: an ontology-based representation of learning object recommendation. In Recent Advances in Information and Communication Technology (pp. 293–303). Springer.

  • Raju, P., & Ahmed, V. (2012). Enabling technologies for developing next-generation learning object repository for construction. Automation in Construction, 22, 247–257.

    Article  Google Scholar 

  • Ranjbar, M., Moradi, P., Azami, M., & Jalili, M. (2015). An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Engineering Applications of Artificial Intelligence, 46, 58–66.

    Article  Google Scholar 

  • Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). Springer.

  • Romero, L., Saucedo, C., Caliusco, M. L., & Gutiérrez, M. (2019). Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths. International Journal of Educational Technology in Higher Education, 16(1), 16.

    Article  Google Scholar 

  • Ruiz-Iniesta, A., Jimenez-Diaz, G., & Gomez-Albarran, M. (2014). A semantically enriched context-aware OER recommendation strategy and its application to a computer science OER repository. IEEE Transactions on Education, 57(4), 255–260.

    Article  Google Scholar 

  • Safoury, L., & Salah, A. (2013). Exploiting user demographic attributes for solving cold-start problem in recommender system. Lecture Notes on Software Engineering, 1(3), 303–307.

    Article  Google Scholar 

  • Saleena, B., & Srivatsa, S. K. (2015). Using concept similarity in cross ontology for adaptive e-learning systems. Journal of King Saud University-Computer and Information Sciences, 27(1), 1–12.

    Article  Google Scholar 

  • Salehi, M., Kamalabadi, I. N., & Ghoushchi, M. B. G. (2013). An effective recommendation framework for personal learning environments using a learner preference tree and a GA. IEEE Transactions on Learning Technologies, 6(4), 350–363.

    Article  Google Scholar 

  • Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285–295).

  • Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291–324). Springer.

  • Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrievaz (pp. 253–260).

  • Senthilnayaki, B., Venkatalakshmi, K., & Kannan, A. (2015). An ontology based framework for intelligent web based e-learning. International Journal of Intelligent Information Technologies (IJIIT), 11(2), 23–39.

    Article  Google Scholar 

  • Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257–297). Springer.

  • Shaw, G., Xu, Y., & Geva, S. (2010). Using association rules to solve the cold-start problem in recommender systems. In Pacific-Asia conference on knowledge discovery and data mining (pp. 340–347). Springer.

  • Sheeba, T., & Krishnan, R. (2016). An ontological framework of semantic learner profile in an e-learning system. International Conference on Brain Inspired Cognitive Systems. (pp. 284–297). Springer.

  • Shishehchi, S., Banihashem, S. Y., Zin, N. A. M., Noah, S. A. M., & Malaysia, K. (2012). Ontological approach in knowledge based recommender system to develop the quality of e-learning system. Australian Journal of Basic and Applied Sciences, 6(2), 115–123.

    Google Scholar 

  • Silva, N., Carvalho, D., Pereira, A. C., Mourão, F., & Rocha, L. (2019). The pure cold-start problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems, 80, 1–12.

    Article  Google Scholar 

  • Soloman, B. A., & Felder, R. M. (2005). Index of learning styles questionnaire. NC State University. Available online at: http://www.engr.ncsu.edu/learningstyles/ilsweb.html. Accessed 14 May 2010, 70.

  • Son, L. H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87–104.

    Article  Google Scholar 

  • Sosnovsky, S., Hsiao, I. H., & Brusilovsky, P. (2012). Adaptation “in the Wild”: ontology-based personalization of open-corpus learning material. European Conference on Technology Enhanced Learning. (pp. 425–431). Springer.

  • Tarus, J. K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37–48.

    Article  Google Scholar 

  • Tarus, J. K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial intelligence review, 50(1), 21–48.

    Article  Google Scholar 

  • Vanitha, V., & Krishnan, P. (2019). A modified ant colony algorithm for personalized learning path construction. Journal of Intelligent & Fuzzy Systems, 37(5), 6785–6800.

    Article  Google Scholar 

  • Victor, P., De Cock, M., Cornelis, C., & Teredesai, A. M. (2008). Getting cold start users connected in a recommender system's trust network. In Computational Intelligence in Decision and Control (pp. 877–882).

  • Wiley, D. A. (2000). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. The instructional use of learning objects, 2830(435), 1–35.

    Google Scholar 

  • Wongchokprasitti, C., Peltonen, J., Ruotsalo, T., Bandyopadhyay, P., Jacucci, G., & Brusilovsky, P. (2015). User model in a box: Cross-system user model transfer for resolving cold start problems. In International Conference on User Modeling, Adaptation, and Personalization (pp. 289–301). Springer.

  • Yang, S. Y. (2010). Developing an ontology-supported information integration and recommendation system for scholars. Expert Systems with Applications, 37(10), 7065–7079.

    Article  Google Scholar 

  • Yao, L., Sheng, Q. Z., Ngu, A. H., Yu, J., & Segev, A. (2014). Unified collaborative and content-based web service recommendation. IEEE Transactions on Services Computing, 8(3), 453–466.

    Article  Google Scholar 

  • Zhang, Z. K., Liu, C., Zhang, Y. C., & Zhou, T. (2010). Solving the cold-start problem in recommender systems with social tags. EPL (Europhysics Letters), 92(2), 28002.

    Article  Google Scholar 

  • Zhao, X., Niu, Z., Chen, W., Shi, C., Niu, K., & Liu, D. (2015a). A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. Journal of Intelligent Information Systems, 44(3), 335–353.

    Article  Google Scholar 

  • Zhao, X., Niu, Z., Wang, K., Niu, K., & Liu, Z. (2015b). Improving top-N recommendation performance using missing data. Mathematical Problems in Engineering, 2015.

  • Zhong, J., Xie, H. & Wang, F.L. (2019). The research trends in recommender systems for e-learning: A systematic review of SSCI journal articles from 2014 to 2018. Asian Association of Open Universities Journal, 14(1), 12–27.

  • Zhuhadar, L., & Nasraoui, O. (2010). A hybrid recommender system guided by semantic user profiles for search in the e-learning domain. Journal of Emerging Technologies in Web Intelligence, 2(4), 272–281.

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  1. Division of Information Technology, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, India

    Joy Jeevamol & V. G. Renumol

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Jeevamol, J., Renumol, V.G. An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Educ Inf Technol 26, 4993–5022 (2021). https://doi.org/10.1007/s10639-021-10508-0

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