Hierarchical Reinforcement Learning for Course Recommendation in MOOCs

Matrix Factorization Techniques for Recommender Systems

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MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs

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The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation

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Diffusion Augmentation for Sequential Recommendation

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Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

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Hierarchical Reinforcement Learning: A Survey and Open Research Challenges

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Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs

Zhao, K., Wang, X., Zhang, Y., Li, Z., Liu, Z., Xing, C., & Xie, X. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401171 · Cited by 113

Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization

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A Survey on Reinforcement Learning for Recommender Systems

Lin, Y., Liu, Y., Lin, F., Zou, L., Wu, P., Zeng, W., Chen, H., & Miao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3280161 · Cited by 125

POCLib: A High-Performance Framework for Enabling Near Orthogonal Processing on Compression

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HGNN: Hyperedge-based graph neural network for MOOC Course Recommendation

Wang, X., Ma, W., Guo, L., Jiang, H., Liu, F., & Xu, C. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102938 · Cited by 95

Layer-refined Graph Convolutional Networks for Recommendation

Zhou, X., Lin, D., Liu, Y., & Miao, C. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00100 · Cited by 100

Adaptive course recommendation in MOOCs

Lin, Y., Feng, S., Lin, F., Zeng, W., Liu, Y., & Wu, P. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107085 · Cited by 83

Hierarchical Reinforcement Learning for Integrated Recommendation

Xie, R., Zhang, S., Wang, R., Xia, F., & Lin, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16580 · Cited by 80

KGAN: Knowledge Grouping Aggregation Network for course recommendation in MOOCs

Zhang, H., Shen, X., Yi, B., Wang, W., & Feng, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118344 · Cited by 81

EduGraph: Learning Path-Based Hypergraph Neural Networks for MOOC Course Recommendation

Li, M., Li, Z., Huang, C., Jiang, Y., & Wu, X. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3453757 · Cited by 75

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3412252 · Cited by 64

Personalized Online Learning Resource Recommendation Based on Artificial Intelligence and Educational Psychology

Wei, X., Sun, S., Wu, D., & Zhou, L. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.767837 · Cited by 59

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

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Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

Guo, L., Zhang, J., Chen, T., Wang, X., & Yin, H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3185101 · Cited by 57

A Tutorial-Generating Method for Autonomous Online Learning

Wu, X., Wang, H., Zhang, Y., Zou, B., & Hong, H. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3390593 · Cited by 65

A literature review of implemented recommendation techniques used in Massive Open online Courses

Khalid, A., Lundqvist, K. Ø., & Yates, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115926 · Cited by 51

Knowledge discovery for course choice decision in Massive Open Online Courses using machine learning approaches

Nilashi, M., Bidgoli, B. M., Alghamdi, A., Alrizq, M., Alghamdi, O., Nayer, F. K., Aljehane, N. O., Khosravi, A., & Mohd, S. B. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117092 · Cited by 49

Deep Reinforcement Learning Framework for Category-Based Item Recommendation

Fu, M., Agrawal, A., Irissappane, A. A., Zhang, J., Huang, L., & Qu, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3089941 · Cited by 48

Knowledge-aware sequence modelling with deep learning for online course recommendation

Deng, W., Zhu, P., Chen, H., Yuan, T., & Wu, J. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103377 · Cited by 45

Enabling cross-continent provider fairness in educational recommender systems

Gómez, E., Zhang, C. S., Boratto, L., Salamó, M., & Ramos, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.025 · Cited by 47

Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network

Zhu, Y., Lin, Q., Lü, H., Shi, K., Liu, D., Chambua, J., Wan, S., & Niu, Z. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3125424 · Cited by 43

Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

Gong, J., Wan, Y., Liu, Y., Li, X., Zhao, Y., Wang, C., Lin, Y., Fang, X., Feng, W., Zhang, J., et al. (2023) · ACM Transactions on the Web · doi:10.1145/3580510 · Cited by 42

Contemporary Recommendation Systems on Big Data and Their Applications: A Survey

Xia, Z., Sun, A., Xu, J., Peng, Y., Ma, R., & Cheng, M. (2024) · IEEE Access · doi:10.1109/access.2024.3517492 · Cited by 64

MOOC performance prediction and personal performance improvement via Bayesian network

Hao, J., Gan, J., & Zhu, L. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10926-8 · Cited by 41

Deep Reinforcement Learning for Sequential Targeting

Wang, W., Li, B., Luo, X., & Wang, X. (2022) · Management Science · doi:10.1287/mnsc.2022.4621 · Cited by 40

Reinforced Explainable Knowledge Concept Recommendation in MOOCs

Jiang, L., Liu, K., Wang, Y., Wang, D., Wang, P., Fu, Y., & Yin, M. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3579991 · Cited by 37

TagRec: Temporal-Aware Graph Contrastive Learning With Theoretical Augmentation for Sequential Recommendation

Peng, T., Yuan, H., Zhang, Y., Li, Y., Dai, P., Wang, Q., Wang, S., & Wu, W. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3538706 · Cited by 39

Privacy-preserving federated learning framework in multimedia courses recommendation

Qin, Y., Li, M., & Zhu, J. (2022) · Wireless Networks · doi:10.1007/s11276-021-02854-1 · Cited by 37

Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation

Tong, X., Wang, P., Li, C., Xia, L., & Niu, S. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/220 · Cited by 36

Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation

Lin, Y., Lin, F., Zeng, W., Xiahou, J., Li, L., Wu, P., Liu, Y., & Miao, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108546 · Cited by 36

Personalized Approximate Pareto-Efficient Recommendation

Xie, R., Liu, Y., Zhang, S., Wang, R., Xia, F., & Lin, L. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450039 · Cited by 35

Capacity Tracing-Enhanced Course Recommendation in MOOCs

Tian, X., & Liu, F. (2021) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2021.3083180 · Cited by 36

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Frej, J., Shah, N., Knežević, M., Nazaretsky, T., & Käser, T. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636898 · Cited by 39

MaHRL

Zhao, D., Zhang, L., Zhang, B., Zheng, L., Bao, Y., & Yan, W. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401170 · Cited by 32

Contrastive Personalized Exercise Recommendation With Reinforcement Learning

Wu, S., Wang, J., & Zhang, W. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3326449 · Cited by 34

Potential Future Directions in Optimization of Students’ Performance Prediction System

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Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

Sun, Y., Wang, B., Sun, Z., & Yang, X. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/218 · Cited by 30

Context-aware reinforcement learning for course recommendation

Lin, Y., Lin, F., Yang, L., Zeng, W., Liu, Y., & Wu, P. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109189 · Cited by 30

Predicting Student Performance in Future Exams via Neutrosophic Cognitive Diagnosis in Personalized E-Learning Environment

Ma, H., Huang, Z., Tang, W., Zhu, H., Zhang, H., & Li, J. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3240931 · Cited by 30

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Sun, Z., Fang, H., Yang, J., Qu, X., Liu, H., Yu, D., Ong, Y.-S., & Zhang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3231891 · Cited by 35

Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning.Preprint

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Integrating learners’ knowledge background to improve course recommendation fairness: A multi-graph recommendation method based on contrastive learning

Ma, W., Chen, W., Lu, L., & Fan, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103750 · Cited by 34

Multi-knowledge enhanced graph convolution for learning resource recommendation

Dong, Y., Liu, Y., Dong, Y., Wang, Y., & Chen, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111521 · Cited by 27

Improving Deep Item-Based Collaborative Filtering with Bayesian Personalized Ranking for MOOC Course Recommendation

Li, X., Li, X., Tang, J., Wang, T., Zhang, Y. A., & Chen, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55130-8_22 · Cited by 26

GuessUNeed

Zhao, Z., Yang, Y., Li, C., & Nie, L. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3410441 · Cited by 25

Graph path fusion and reinforcement reasoning for recommendation in MOOCs

Liang, Z., Mu, L., Chen, J., & Xie, Q. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11178-2 · Cited by 27

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

Chen, X., Yao, L., McAuley, J., Zhou, G., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03540 · Cited by 23

KPCR: Knowledge Graph Enhanced Personalized Course Recommendation

Jung, H., Jang, Y., Kim, S., & Kim, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_60 · Cited by 23

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Zhang, X., Li, H., Liu, Z., Chen, Z., Zhou, H., Rong, W., & Ouyang, Y. (2024) · Frontiers of digital education. · doi:10.1007/s44366-024-0019-6 · Cited by 23

Hierarchical reinforcement learning for automatic disease diagnosis

Zhong, C., Liao, K., Chen, W., Liu, Q., Peng, B., Huang, X., Peng, J., & Wei, Z. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac408 · Cited by 43

BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning

Bi, H., Chen, E., He, W., Wu, H., Zhao, W., Wang, S., & Wu, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25629 · Cited by 21

Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation

Li, Q., Xia, W., Yin, L., Shen, J., Rui, R., Zhang, W., Chen, X., Tang, R., & Yu, Y. H. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614897 · Cited by 25

ACMF: An Attention Collaborative Extended Matrix Factorization Based Model for MOOC course service via a heterogeneous view

Sheng, D., Yuan, J., Xie, Q., & Li, L. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.001 · Cited by 21

Inferring substitutable and complementary products with Knowledge-Aware Path Reasoning based on dynamic policy network

Yang, Z., Ye, J., Wang, L., Lin, X. A., & He, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107579 · Cited by 20

Knowledge-aware reasoning with self-supervised reinforcement learning for explainable recommendation in MOOCs

Lin, Y., Zhang, W., Lin, F., Zeng, W., Zhou, X., & Wu, P. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09257-7 · Cited by 22

A Study of Deep Reinforcement Learning Based Recommender Systems

Gupta, G., & Katarya, R. (2021) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc51823.2021.9478178 · Cited by 18

HGCR: A Heterogeneous Graph-Enhanced Interactive Course Recommendation Scheme for Online Learning

Wang, Y., Ma, D., Ma, J., & Jin, Q. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3314399 · Cited by 18

Course Recommender Systems Need to Consider the Job Market

Frej, J., Dai, A., Montariol, S., Bosselut, A., & Käser, T. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657847 · Cited by 22

KnowPath: An LLM-Supported Knowledge Graph Construction and Path Finding Framework to Explainable MOOC Recommendations

Zhu, J., Chen, Z., Meo, P. D., Guan, J., Han, Z., & Shi, W. (2025) · ACM Transactions on Information Systems · doi:10.1145/3779436 · Cited by 16

Multi-scale reinforced profile for personalized recommendation with deep neural networks in MOOCs

Lin, Y., Feng, S., Lin, F., Xiahou, J., & Zeng, W. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110905 · Cited by 16

Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach

Zheng, Z., Sun, Y., Song, X. Y., Zhu, H., & Xiong, H. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3608795 · Cited by 15

Towards prescriptive analytics of self‐regulated learning strategies: A reinforcement learning approach

Osakwe, I., Chen, G., Fan, Y., Raković, M., Singh, S., Lim, L., van der Graaf, J., Moore, J. D., Molenaar, I., Bannert, M., et al. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13429 · Cited by 17

LE-DLCM: Decoupled learner and course modeling with large language models for enhanced course recommendation

Ma, J., Zhao, Z., Xie, Z., Zhang, Y., & Zhou, G. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115135 · Cited by 18

A transformer–convolution model for enhanced session-based recommendation

Wang, J., Xie, H., Wang, F. L., & Lee, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.083 · Cited by 14

Top-N Knowledge Concept Recommendations in MOOCs Using a Neural Co-Attention Model

Klašnja‐Milićević, A., & Milicevic, D. (2023) · IEEE Access · doi:10.1109/access.2023.3278609 · Cited by 14

Knowledge Graph-Based Behavior Denoising and Preference Learning for Sequential Recommendation

Liu, H., Zhu, Y., & Wu, Z. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3325666 · Cited by 14

Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation

Zhang, Z., Xiao, Y., Jiang, L., Yang, D., Yin, M., & Wang, P. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28793 · Cited by 14

DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning

Lin, R., Tang, F., He, C., Wu, Z., Yuan, C., & Tang, Y. Z. (2023) · World Wide Web · doi:10.1007/s11280-022-01135-x · Cited by 14

Attentional Autoencoder for Course Recommendation in MOOC with Course Relevance

Tan, J., Chang, L., Liu, T., & Zhao, X. (2020) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc49757.2020.00038 · Cited by 13

Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Gong, J., Wang, C., Zhao, Z., & Zhang, X. (2021) · Electronics · doi:10.3390/electronics10141671 · Cited by 13

Towards attribute-Augmented course recommendation: An LLM-Driven model-Agnostic representation learning framework

Zhou, G., Zhao, Z., Ma, J., & Xie, Z. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115809 · Cited by 12

Reinforcement learning-based denoising network for sequential recommendation

Tong, X., Wang, P., & Niu, S. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03298-6 · Cited by 12

Towards Better Course Recommendations: Integrating Multi-Perspective Meta-Paths and Knowledge Graphs

Yang, T. Q., Ren, B., Gu, C., Ma, B., He, T., & Konomi, S. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706486 · Cited by 12

Personalized learning path based on graph attention mechanism deep reinforcement learning research on recommender systems

Gu, R. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978241313260 · Cited by 11

Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library

Wang, X., Wang, Y.-C., Guo, L., Xu, L., Gao, B., Liu, F., & Li, W. (2021) · Information · doi:10.3390/info12050198 · Cited by 11

Scratch‐RL: A preference‐driven adversarial reinforcement reasoning framework over knowledge graphs for explainable recommendation of Scratch

Qi, P., Sun, Y., & Luo, H. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22931 · Cited by 11

Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review

Narimani, A., & Barberà, E. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i1.7419 · Cited by 10

MCAP: Low-Pass GNNs with Matrix Completion for Academic Recommendations

Zhang, D., Zheng, S., Zhu, Y., Yuan, H., Gong, J., & Tang, J. (2024) · ACM Transactions on Information Systems · doi:10.1145/3698193 · Cited by 10

Course Recommendation System Based on Course Knowledge Graph Generated by Large Language Models

Chen, X., Yin, C., Chen, H., Rong, W., Ouyang, Y., & Chai, Y. (2024) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale62452.2024.10834324 · Cited by 9

Self‐supervised graph learning for occasional group recommendation

Hao, B., Yin, H., Li, C., & Chen, H. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23011 · Cited by 9

A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems

Cavenaghi, E., Sottocornola, G., Stella, F. A., & Zanker, M. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3596519 · Cited by 9

EduLGCL: Local-global contrastive learning model for education recommendation

Zhao, Y., Jiang, F., Pang, Y., Deng, Y., Han, Y., & Wang, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111357 · Cited by 10

An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation

Liang, K., Zhang, G., Guo, J., & Li, W. (2023) · Electronics · doi:10.3390/electronics12244939 · Cited by 8

A Review on Reinforcement Learning based News Recommendation Systems and its challenges

Bangari, S., Nayak, S., Patel, L., & Rashmi, K. T. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395812 · Cited by 8

A MOOCs Recommender System Based on User’s Knowledge Background

Zhao, Y., Ma, W., Jiang, Y., & Zhan, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82136-4_12 · Cited by 8

Learning From Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

Du, Z., Yang, N., Yu, Z., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3144292 · Cited by 8

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

Madhavi, A., Nagesh, A., & Govardhan, A. (2023) · ICST Transactions on e-Education and e-Learning · doi:10.4108/eetel.4470 · Cited by 7

Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective

Zhang, H., Shen, S., Xu, B., Huang, Z., Wu, J., Sha, J. J., & Wang, S. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671947 · Cited by 14

Predicting Instructor Performance in Online Education: An Interpretable Hierarchical Transformer with Contextual Attention

Wang, W., Zhou, M., Li, B., & Zhuang, H. (2025) · Information Systems Research · doi:10.1287/isre.2021.0310 · Cited by 8

Personalized Learning Path Recommendation with Time-Aware Attention-Based Reinforcement Learning

Jiang, S., Wen, Y., Shen, J., Peng, G., Kang, G., & Liu, J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3747594 · Cited by 7

MHRR: MOOCs Recommender Service With Meta Hierarchical Reinforced Ranking

Li, Y., Xiong, H., Kong, L., Zhang, R., Xu, F., Chen, G., & Li, M. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3325302 · Cited by 17

Meta Hierarchical Reinforced Learning to Rank for Recommendation: A Comprehensive Study in MOOCs

Li, Y., Xiong, H., Kong, L., Zhang, R., Dou, D., & Chen, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26422-1_19 · Cited by 8

Hierarchical Reinforcement Learning on Multi-Channel Hypergraph Neural Network for Course Recommendation

Jiang, L., Xiao, Y., Zhao, X., Xu, Y., Hu, S., Wang, P., & Yin, M. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/232 · Cited by 3

Multi-scale reinforced profile for personalized recommendation with deep neural networks in MOOCs

Lin, Y., Feng, S., Lin, F., Xiahou, J., & Zeng, W. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110905 · Cited by 16

Context-aware reinforcement learning for course recommendation

Lin, Y., Lin, F., Yang, L., Zeng, W., Liu, Y., & Wu, P. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109189 · Cited by 30