Xiaoxuan Shen
Orcid: 0000-0002-6663-5821
According to our database1,
Xiaoxuan Shen
authored at least 25 papers
between 2016 and 2024.
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Bibliography
2024
IEEE Trans. Evol. Comput., August, 2024
User Model. User Adapt. Interact., July, 2024
IEEE Trans. Neural Networks Learn. Syst., June, 2024
Automated discovery of symbolic laws governing skill acquisition from naturally occurring data.
Nat. Comput. Sci., May, 2024
Heterogeneous Evolution Network Embedding with Temporal Extension for Intelligent Tutoring Systems.
ACM Trans. Inf. Syst., March, 2024
Weighted Heterogeneous Graph-Based Three-View Contrastive Learning for Knowledge Tracing in Personalized e-Learning Systems.
IEEE Trans. Consumer Electron., February, 2024
Knowledge-aware fine-grained attention networks with refined knowledge graph embedding for personalized recommendation.
Expert Syst. Appl., 2024
Expert Syst. Appl., 2024
Contrastive multi-interest graph attention network for knowledge-aware recommendation.
Expert Syst. Appl., 2024
CoRR, 2024
COMET: "Cone of experience" enhanced large multimodal model for mathematical problem generation.
CoRR, 2024
Proceedings of the ACM on Web Conference 2024, 2024
Proceedings of the International Joint Conference on Neural Networks, 2024
2023
RIECN: learning relation-based interactive embedding convolutional network for knowledge graph.
Neural Comput. Appl., April, 2023
IEEE Trans. Ind. Informatics, 2023
Knowl. Based Syst., 2023
Expert Syst. Appl., 2023
Proceedings of the 31st ACM International Conference on Multimedia, 2023
2022
EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender System.
IEEE Trans. Ind. Informatics, 2022
Neurocomputing, 2022
2021
Deep Variational Matrix Factorization with Knowledge Embedding for Recommendation System.
IEEE Trans. Knowl. Data Eng., 2021
2019
Deep Matrix Factorization With Implicit Feedback Embedding for Recommendation System.
IEEE Trans. Ind. Informatics, 2019
2018
2016
Expanded autoencoder recommendation framework and its application in movie recommendation.
Proceedings of the 10th International Conference on Software, 2016