Session-based Methods for Course Recommendation
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Published
Jun 27, 2024
Md Akib Zabed Khan
Agoritsa Polyzou
https://orcid.org/0009-0002-0706-8019
Abstract
In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using different data mining techniques and machine learning algorithms utilizing different data types. One important aspect of the data is that usually, courses taken together in a semester fit well with each other. If there is no correlation between the co-taken courses, students may find it more difficult to handle the workload. Based on this insight, we propose using session-based approaches to recommend a set of well-suited courses for the upcoming semester. We test three session-based course recommendation models, two based on neural networks (CourseBEACON and CourseDREAM) and one on tensor factorization (TF-CoC). Additionally, we propose a post-processing approach to adjust the recommendation scores of any base course recommender to promote related courses. Using metrics capturing different aspects of the recommendation quality, our experimental evaluation shows that session-based methods outperform existing popularity-based, association-based, similarity-based, factorization-based, neural networks-based, and Markov chain-based recommendation approaches. Effective course recommendations can result in improved student advising, which, in turn, can improve student performance, decrease dropout rates, and a more positive overall student experience and satisfaction.
How to Cite
Khan, M. A. Z., & Polyzou, A. (2024). Session-based Methods for Course Recommendation. Journal of Educational Data Mining, 16(1), 164–196. https://doi.org/10.5281/zenodo.11384740
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Keywords
session-based recommendation, course recommendation, student enrollment data, deep learning
References
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BENDAKIR, N. AND AÏMEUR, E. 2006. Using association rules for course recommendation. In Proceedings of the AAAI workshop on educational data mining. Vol. 3. Citeseer, 1–10.
BERNHARDSSON, E. 2023. Approximate Nearest Neighbors. Spotify. https://github.com/ spotify/annoy.
BOUMI, S. AND VELA, A. E. 2021. Quantifying the impact of students’ semester course load on their academic performance. In 2021 ASEE Virtual Annual Conference Content Access.
BROWN, M. G., DEMONBRUN, R. M., AND TEASLEY, S. D. 2018. Conceptualizing co-enrollment: Accounting for student experiences across the curriculum. In Proceedings of the 8th international conference on learning analytics and knowledge. ACM New York, NY, United States, 305–309.
CHEN, Q., GUO, Z., LI, J., AND LI, G. 2023. Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 352–361.
CHOCKKALINGAM, S., YU, R., AND PARDOS, Z. A. 2021. Which one’s more work? predicting effective credit hours between courses. In LAK21: 11th International Learning Analytics and Knowledge Conference. ACM New York, NY, United States, 599–605.
DE MEDIO, C., LIMONGELLI, C., SCIARRONE, F., AND TEMPERINI, M. 2020. Moodlerec: A recommendation system for creating courses using the moodle e-learning platform. Computers in Human Behavior 104, 106168.
DIAMOND, A., ROBERTS, J., VORLEY, T., BIRKIN, G., EVANS, J., SHEEN, J., AND NATHWANI, T. 2014. Uk review of the provision of information about higher education: advisory study and literature review: report to the uk higher education funding bodies by cfe research. Leicester: CRE Research.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM conference on recommender systems. ACM New York, NY, USA, 183–190.
ENTEZARI, N., PAPALEXAKIS, E. E., WANG, H., RAO, S., AND PRASAD, S. K. 2021. Tensor-based complementary product recommendation. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 409–415.
ESTEBAN, A., ZAFRA, A., AND ROMERO, C. 2018. A hybrid multi-criteria approach using a genetic algorithm for recommending courses to university students. In Proceedings of the 11th International Conference on Educational Data Mining, K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, 273–279.
GAO, M., LUO, Y., AND HU, X. 2022. Online course recommendation using deep convolutional neural network with negative sequence mining. Wireless Communications and Mobile Computing 2022.
GARDNER, J. AND BROOKS, C. 2018. Coenrollment networks and their relationship to grades in undergraduate education. In Proceedings of the 8th international conference on learning analytics and knowledge. ACM New York, NY, United States, 295–304.
GONG, T., LI, J., YEUNG, J. Y., AND ZHANG, X. 2024. The association between course selection and academic performance: exploring psychological interpretations. Studies in Higher Education, 1–14.
GUO, L., YIN, H., WANG, Q., CHEN, T., ZHOU, A., AND QUOC VIET HUNG, N. 2019. Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM New York, NY, USA, 1569–1577.
HANSON, M. 2022. College Dropout Rates. EducationData.org. https://educationdata.org/ college-dropout-rates.
HU, H., HE, X., GAO, J., AND ZHANG, Z.-L. 2020. Modeling personalized item frequency information for next-basket recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 1071–1080.
HU, Y., KOREN, Y., AND VOLINSKY, C. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. IEEE, 263–272.
JIANG, W. AND PARDOS, Z. A. 2020. Evaluating sources of course information and models of representation on a variety of institutional prediction tasks. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 115–125.
JIANG, W., PARDOS, Z. A., AND WEI, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th international conference on learning analytics & knowledge. ACM New York, NY, USA, 36–45.
KADLEC, A., IMMERWAHR, J., AND GUPTA, J. 2014. Guided pathways to student success perspectives from indiana college students and advisors. New York: Public Agenda.
KHORASANI, E. S., ZHENGE, Z., AND CHAMPAIGN, J. 2016. A markov chain collaborative filtering model for course enrollment recommendations. In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 3484–3490.
KIPF, T. N. AND WELLING, M. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24- 26, 2017, Conference Track Proceedings. OpenReview.net.
LE, D.-T., LAUW, H. W., AND FANG, Y. 2019. Correlation-sensitive next-basket recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 2808–2814.
MA, B., LU, M., TANIGUCHI, Y., AND KONOMI, S. 2021. Exploration and explanation: An interactive course recommendation system for university environments. In CEUR Workshop Proceedings. Vol. 2903. CEUR-WS.
MA, B., TANIGUCHI, Y., AND KONOMI, S. 2020. Course recommendation for university environments. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 460–466.
MALHOTRA, I., CHANDRA, P., AND LAVANYA, R. 2022. Course recommendation using domain-based cluster knowledge and matrix factorization. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 12–18.
MONDAL, B., PATRA, O., MISHRA, S., AND PATRA, P. 2020. A course recommendation system based on grades. In 2020 international conference on computer science, engineering and applications (ICCSEA). IEEE, 1–5.
MORSOMME, R. AND ALFEREZ, S. V. 2019. Content-based course recommender system for liberal arts education. In Proceedings of the 12th International Conference on Educational Data Mining, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 748–753.
MORSY, S. AND KARYPIS, G. 2019. Will this course increase or decrease your gpa? towards gradeaware course recommendation. Journal of Educational Data Mining 11, 2, 20–46.
NAREN, J., BANU, M. Z., AND LOHAVANI, S. 2020. Recommendation system for students’ course selection. In Smart Systems and IoT: Innovations in Computing, A. K. Somani, R. S. Shekhawat, A. Mundra, S. Srivastava, and V. K. Verma, Eds. Springer, 825–834.
OBEIDAT, R., DUWAIRI, R., AND AL-AIAD, A. 2019. A collaborative recommendation system for online courses recommendations. 2019. In International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 49–54.
PARAMESWARAN, A., VENETIS, P., AND GARCIA-MOLINA, H. 2011. Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems (TOIS) 29, 4, 1–33.
PARDOS, Z. A., FAN, Z., AND JIANG, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User modeling and useradapted interaction 29, 2, 487–525.
PARDOS, Z. A. AND JIANG, W. 2020. Designing for serendipity in a university course recommendation system. In Proceedings of the tenth international conference on learning analytics & knowledge. ACM New York, NY, United States, 350–359.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., ET AL. 2011. Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830.
POLYZOU, A., NIKOLAKOPOULOS, A. N., AND KARYPIS, G. 2019. Scholars walk: A markov chain framework for course recommendation. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 396–401.
REN, Z., NING, X., LAN, A. S., AND RANGWALA, H. 2019. Grade prediction based on cumulative knowledge and co-taken courses. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 158–167.
RENDLE, S., FREUDENTHALER, C., GANTNER, Z., AND SCHMIDT-THIEME, L. 2014. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009). Cornell University, NY, USA.
RENDLE, S., FREUDENTHALER, C., AND SCHMIDT-THIEME, L. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. ACM New York, NY, United States, 811-820.
SHAO, E., GUO, S., AND PARDOS, Z. A. 2021. Degree planning with plan-bert: Multi-semester recommendation using future courses of interest. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. AAAI Press, Palo Alto, California USA, 14920–14929.
SHAO, Z., WANG, S., ZHANG, Q., LU, W., LI, Z., AND PENG, X. 2023. An empirical study of nextbasket recommendations. arXiv preprint arXiv:2312.02550.
SHEN, Y., OU, B., AND LI, R. 2022. Mbn: Towards multi-behavior sequence modeling for next basket recommendation. ACM Transactions on Knowledge Discovery from Data (TKDD) 16, 5, 1–23.
SULAIMAN, M. S., TAMIZI, A. A., SHAMSUDIN, M. R., AND AZMI, A. 2020. Course recommendation system using fuzzy logic approach. Indonesian Journal of Electrical Engineering and Computer Science 17, 1, 365–371.
SYMEONIDIS, P. AND MALAKOUDIS, D. 2019. Multi-modal matrix factorization with side information for recommending massive open online courses. Expert Systems with Applications 118, 261–271.
VIRTANEN, P., GOMMERS, R., OLIPHANT, T. E., HABERLAND, M., REDDY, T., COURNAPEAU, D., BUROVSKI, E., PETERSON, P., WECKESSER, W., BRIGHT, J., VAN DER WALT, S. J., BRETT, M., WILSON, J., MILLMAN, K. J., MAYOROV, N., NELSON, A. R. J., JONES, E., KERN, R., LARSON, E., CAREY, C. J., POLAT, Í., FENG, Y., MOORE, E. W., VANDERPLAS, J., LAXALDE, D., PERKTOLD, J., CIMRMAN, R., HENRIKSEN, I., QUINTERO, E. A., HARRIS, C. R., ARCHIBALD, A. M., RIBEIRO, A. H., PEDREGOSA, F., VAN MULBREGT, P., AND SCIPY 1.0 CONTRIBUTORS. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261–272.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2022. Personalized and explainable course recommendations for students at risk of dropping out. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, 657–661.
WAN, M.,WANG, D., LIU, J., BENNETT, P., AND MCAULEY, J. 2018. Representing and recommending shopping baskets with complementarity, compatibility and loyalty. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM New York, NY, United States, 1133–1142.
WANG, S., CAO, L., WANG, Y., SHENG, Q. Z., ORGUN, M. A., AND LIAN, D. 2021. A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54, 7, 1–38.
WARNES, Z. AND SMIRNOV, E. 2020. Course recommender systems with statistical confidence. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 509–515.
WONG, C. 2018. Sequence based course recommender for personalized curriculum planning. In International Conference on Artificial Intelligence in Education. Springer, 531–534.
YU, F., LIU, Q., WU, S., WANG, L., AND TAN, T. 2016. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 729–732.
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ARIANNEZHAD, M., JULLIEN, S., LI, M., FANG, M., SCHELTER, S., AND DE RIJKE, M. 2022. Recanet: A repeat consumption-aware neural network for next basket recommendation in grocery shopping. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 1240–1250.
BENDAKIR, N. AND AÏMEUR, E. 2006. Using association rules for course recommendation. In Proceedings of the AAAI workshop on educational data mining. Vol. 3. Citeseer, 1–10.
BERNHARDSSON, E. 2023. Approximate Nearest Neighbors. Spotify. https://github.com/ spotify/annoy.
BOUMI, S. AND VELA, A. E. 2021. Quantifying the impact of students’ semester course load on their academic performance. In 2021 ASEE Virtual Annual Conference Content Access.
BROWN, M. G., DEMONBRUN, R. M., AND TEASLEY, S. D. 2018. Conceptualizing co-enrollment: Accounting for student experiences across the curriculum. In Proceedings of the 8th international conference on learning analytics and knowledge. ACM New York, NY, United States, 305–309.
CHEN, Q., GUO, Z., LI, J., AND LI, G. 2023. Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 352–361.
CHOCKKALINGAM, S., YU, R., AND PARDOS, Z. A. 2021. Which one’s more work? predicting effective credit hours between courses. In LAK21: 11th International Learning Analytics and Knowledge Conference. ACM New York, NY, United States, 599–605.
DE MEDIO, C., LIMONGELLI, C., SCIARRONE, F., AND TEMPERINI, M. 2020. Moodlerec: A recommendation system for creating courses using the moodle e-learning platform. Computers in Human Behavior 104, 106168.
DIAMOND, A., ROBERTS, J., VORLEY, T., BIRKIN, G., EVANS, J., SHEEN, J., AND NATHWANI, T. 2014. Uk review of the provision of information about higher education: advisory study and literature review: report to the uk higher education funding bodies by cfe research. Leicester: CRE Research.
ELBADRAWY, A. AND KARYPIS, G. 2016. Domain-aware grade prediction and top-n course recommendation. In Proceedings of the 10th ACM conference on recommender systems. ACM New York, NY, USA, 183–190.
ENTEZARI, N., PAPALEXAKIS, E. E., WANG, H., RAO, S., AND PRASAD, S. K. 2021. Tensor-based complementary product recommendation. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 409–415.
ESTEBAN, A., ZAFRA, A., AND ROMERO, C. 2018. A hybrid multi-criteria approach using a genetic algorithm for recommending courses to university students. In Proceedings of the 11th International Conference on Educational Data Mining, K. E. Boyer and M. Yudelson, Eds. International Educational Data Mining Society, 273–279.
GAO, M., LUO, Y., AND HU, X. 2022. Online course recommendation using deep convolutional neural network with negative sequence mining. Wireless Communications and Mobile Computing 2022.
GARDNER, J. AND BROOKS, C. 2018. Coenrollment networks and their relationship to grades in undergraduate education. In Proceedings of the 8th international conference on learning analytics and knowledge. ACM New York, NY, United States, 295–304.
GONG, T., LI, J., YEUNG, J. Y., AND ZHANG, X. 2024. The association between course selection and academic performance: exploring psychological interpretations. Studies in Higher Education, 1–14.
GUO, L., YIN, H., WANG, Q., CHEN, T., ZHOU, A., AND QUOC VIET HUNG, N. 2019. Streaming session-based recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM New York, NY, USA, 1569–1577.
HANSON, M. 2022. College Dropout Rates. EducationData.org. https://educationdata.org/ college-dropout-rates.
HU, H., HE, X., GAO, J., AND ZHANG, Z.-L. 2020. Modeling personalized item frequency information for next-basket recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, United States, 1071–1080.
HU, Y., KOREN, Y., AND VOLINSKY, C. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. IEEE, 263–272.
JIANG, W. AND PARDOS, Z. A. 2020. Evaluating sources of course information and models of representation on a variety of institutional prediction tasks. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 115–125.
JIANG, W., PARDOS, Z. A., AND WEI, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th international conference on learning analytics & knowledge. ACM New York, NY, USA, 36–45.
KADLEC, A., IMMERWAHR, J., AND GUPTA, J. 2014. Guided pathways to student success perspectives from indiana college students and advisors. New York: Public Agenda.
KHORASANI, E. S., ZHENGE, Z., AND CHAMPAIGN, J. 2016. A markov chain collaborative filtering model for course enrollment recommendations. In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 3484–3490.
KIPF, T. N. AND WELLING, M. 2017. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24- 26, 2017, Conference Track Proceedings. OpenReview.net.
LE, D.-T., LAUW, H. W., AND FANG, Y. 2019. Correlation-sensitive next-basket recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 2808–2814.
MA, B., LU, M., TANIGUCHI, Y., AND KONOMI, S. 2021. Exploration and explanation: An interactive course recommendation system for university environments. In CEUR Workshop Proceedings. Vol. 2903. CEUR-WS.
MA, B., TANIGUCHI, Y., AND KONOMI, S. 2020. Course recommendation for university environments. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 460–466.
MALHOTRA, I., CHANDRA, P., AND LAVANYA, R. 2022. Course recommendation using domain-based cluster knowledge and matrix factorization. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 12–18.
MONDAL, B., PATRA, O., MISHRA, S., AND PATRA, P. 2020. A course recommendation system based on grades. In 2020 international conference on computer science, engineering and applications (ICCSEA). IEEE, 1–5.
MORSOMME, R. AND ALFEREZ, S. V. 2019. Content-based course recommender system for liberal arts education. In Proceedings of the 12th International Conference on Educational Data Mining, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 748–753.
MORSY, S. AND KARYPIS, G. 2019. Will this course increase or decrease your gpa? towards gradeaware course recommendation. Journal of Educational Data Mining 11, 2, 20–46.
NAREN, J., BANU, M. Z., AND LOHAVANI, S. 2020. Recommendation system for students’ course selection. In Smart Systems and IoT: Innovations in Computing, A. K. Somani, R. S. Shekhawat, A. Mundra, S. Srivastava, and V. K. Verma, Eds. Springer, 825–834.
OBEIDAT, R., DUWAIRI, R., AND AL-AIAD, A. 2019. A collaborative recommendation system for online courses recommendations. 2019. In International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 49–54.
PARAMESWARAN, A., VENETIS, P., AND GARCIA-MOLINA, H. 2011. Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems (TOIS) 29, 4, 1–33.
PARDOS, Z. A., FAN, Z., AND JIANG, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User modeling and useradapted interaction 29, 2, 487–525.
PARDOS, Z. A. AND JIANG, W. 2020. Designing for serendipity in a university course recommendation system. In Proceedings of the tenth international conference on learning analytics & knowledge. ACM New York, NY, United States, 350–359.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., ET AL. 2011. Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825–2830.
POLYZOU, A., NIKOLAKOPOULOS, A. N., AND KARYPIS, G. 2019. Scholars walk: A markov chain framework for course recommendation. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 396–401.
REN, Z., NING, X., LAN, A. S., AND RANGWALA, H. 2019. Grade prediction based on cumulative knowledge and co-taken courses. In 12th International Conference on Educational Data Mining, EDM 2019, C. F. Lynch, A. Merceron, M. Desmarais, and R. Nkambou, Eds. International Educational Data Mining Society, 158–167.
RENDLE, S., FREUDENTHALER, C., GANTNER, Z., AND SCHMIDT-THIEME, L. 2014. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009). Cornell University, NY, USA.
RENDLE, S., FREUDENTHALER, C., AND SCHMIDT-THIEME, L. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. ACM New York, NY, United States, 811-820.
SHAO, E., GUO, S., AND PARDOS, Z. A. 2021. Degree planning with plan-bert: Multi-semester recommendation using future courses of interest. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. AAAI Press, Palo Alto, California USA, 14920–14929.
SHAO, Z., WANG, S., ZHANG, Q., LU, W., LI, Z., AND PENG, X. 2023. An empirical study of nextbasket recommendations. arXiv preprint arXiv:2312.02550.
SHEN, Y., OU, B., AND LI, R. 2022. Mbn: Towards multi-behavior sequence modeling for next basket recommendation. ACM Transactions on Knowledge Discovery from Data (TKDD) 16, 5, 1–23.
SULAIMAN, M. S., TAMIZI, A. A., SHAMSUDIN, M. R., AND AZMI, A. 2020. Course recommendation system using fuzzy logic approach. Indonesian Journal of Electrical Engineering and Computer Science 17, 1, 365–371.
SYMEONIDIS, P. AND MALAKOUDIS, D. 2019. Multi-modal matrix factorization with side information for recommending massive open online courses. Expert Systems with Applications 118, 261–271.
VIRTANEN, P., GOMMERS, R., OLIPHANT, T. E., HABERLAND, M., REDDY, T., COURNAPEAU, D., BUROVSKI, E., PETERSON, P., WECKESSER, W., BRIGHT, J., VAN DER WALT, S. J., BRETT, M., WILSON, J., MILLMAN, K. J., MAYOROV, N., NELSON, A. R. J., JONES, E., KERN, R., LARSON, E., CAREY, C. J., POLAT, Í., FENG, Y., MOORE, E. W., VANDERPLAS, J., LAXALDE, D., PERKTOLD, J., CIMRMAN, R., HENRIKSEN, I., QUINTERO, E. A., HARRIS, C. R., ARCHIBALD, A. M., RIBEIRO, A. H., PEDREGOSA, F., VAN MULBREGT, P., AND SCIPY 1.0 CONTRIBUTORS. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17, 261–272.
WAGNER, K., MERCERON, A., SAUER, P., AND PINKWART, N. 2022. Personalized and explainable course recommendations for students at risk of dropping out. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, 657–661.
WAN, M.,WANG, D., LIU, J., BENNETT, P., AND MCAULEY, J. 2018. Representing and recommending shopping baskets with complementarity, compatibility and loyalty. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM New York, NY, United States, 1133–1142.
WANG, S., CAO, L., WANG, Y., SHENG, Q. Z., ORGUN, M. A., AND LIAN, D. 2021. A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54, 7, 1–38.
WARNES, Z. AND SMIRNOV, E. 2020. Course recommender systems with statistical confidence. In 13th International Conference on Educational Data Mining, EDM 2020, A. N. Rafferty, J. Whitehill, C. Romero, and V. Cavalli-Sforza, Eds. International Educational Data Mining Society, 509–515.
WONG, C. 2018. Sequence based course recommender for personalized curriculum planning. In International Conference on Artificial Intelligence in Education. Springer, 531–534.
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