Zhuang Liu

Orcid: 0000-0001-6149-9667

Affiliations:
  • Beihang University, State Key Laboratory of Software Development Environment, Beijing, China


According to our database1, Zhuang Liu authored at least 14 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Multimodal Contrastive Transformer for Explainable Recommendation.
IEEE Trans. Comput. Soc. Syst., April, 2024

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects.
CoRR, 2024

2023
Reinforcement Learning-Based Recommendation with User Reviews on Knowledge Graphs.
Proceedings of the Knowledge Science, Engineering and Management, 2023

Debiased Contrastive Loss for Collaborative Filtering.
Proceedings of the Knowledge Science, Engineering and Management, 2023

Multi-level and Multi-interest User Interest Modeling for News Recommendation.
Proceedings of the Knowledge Science, Engineering and Management, 2023

PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022
CDARL: a contrastive discriminator-augmented reinforcement learning framework for sequential recommendations.
Knowl. Inf. Syst., 2022

Reinforcement Learning-Based Explainable Recommendation over Knowledge Graphs with Negative Sampling.
Proceedings of the IEEE Smartworld, 2022

MAKT: Multichannel Attention Networks based Knowledge Tracing with Representation Learning.
Proceedings of the IEEE International Conference on Teaching, 2022

Multi-Modal Contrastive Pre-training for Recommendation.
Proceedings of the ICMR '22: International Conference on Multimedia Retrieval, Newark, NJ, USA, June 27, 2022

2021
Contrastive Learning for Recommender System.
CoRR, 2021

Insight into traffic security: A correlation discovery of urban spatial features and traffic flow patterns.
Proceedings of the 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, 2021

2020
Predict the Next Attack Location via An Attention-based Fused-SpatialTemporal LSTM.
Proceedings of the 29th International Conference on Computer Communications and Networks, 2020

2018
MEMN: Multiple Vectors Embedding for Multi-Label Networks.
IEEE Access, 2018


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