Dr Zaiqiao Meng

  • Lecturer, BetaRecSys Project Collaborator (School of Computing Science)

Biography

I am currently a Lecturer (Assistant Professor) of the University of Glasgow, based within the world-leading Information Retrieval group and IDA section of School of Computing Science. I am also an affiliated lecturer at the Language Technology Lab of University of Cambridge. Prior to that, I was a Postdoctoral Researcher (Research Associate) at the Language Technology Laboratory (LTL) of the University of Cambridge; and a Postdoctoral Researcher (Research Assistant ) at the Terrier team of the University of Glasgow (UoG). 

I obtained my Ph.D in computer science from Sun Yat-sen University (SYSU) in December 2018. I also have been a visiting PhD student at the MINE lab of KAUST.

 

Research interests

My research focuses on the intersection of machine learning, knowledge graph, and natural language processing, with a current emphasis on the biomedical applications.

  • Geometric Deep Learning (Graph Neural Networks).
  • Knowledge Graph Construction, Utilisation and Reasoning.
  • Natural Language Processing.
  • Recommender Systems.
  • Healthcare Related Applications (e.g. Disease Outbreak Surveillance and Drug Discovery).

Publications

List by: Type | Date

Jump to: 2022 | 2021 | 2020 | 2019
Number of items: 15.

2022

Chen, G., Liu, F., Meng, Z. and Liang, S. (2022) Revisiting Parameter-Efficient Tuning: Are We Really There Yet? In: 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, 7-11 Dec 2022, (Accepted for Publication)

Liu, S., Meng, Z. , Macdonald, C. and Ounis, I. (2022) Graph neural pre-training for recommendation with side information. ACM Transactions on Information Systems, (Accepted for Publication)

Wang, Y., Chen, X., Fang, J., Meng, Z. and Liang, S. (2022) Enhancing conversational recommendation systems with representation fusion. ACM Transactions on the Web, (Accepted for Publication)

Meng, Z. et al. (2022) BioCaster in 2021: automatic disease outbreaks detection from global news media. Bioinformatics, 38(18), pp. 4446-4448. (doi: 10.1093/bioinformatics/btac497) (PMID:35900173) (PMCID:PMC9477518)

Tang, S., Meng, Z. and Liang, S. (2022) Dynamic co-embedding model for temporal attributed networks. IEEE Transactions on Neural Networks and Learning Systems, (doi: 10.1109/TNNLS.2022.3193564) (Early Online Publication)

Meng, Z. , Liu, F., Shareghi, E., Su, Y., Collins, C. and Collier, N. (2022) Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models. In: 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Dublin, Ireland, 22-27 May 2022, pp. 4798-4810.

Wang, Y., Xin, X., Meng, Z. , Jose, J. M. , Feng, F. and He, X. (2022) Learning Robust Recommenders Through Cross-Model Agreement. In: ACM Web Conference 2022, Lyon, France, 25-29 April 2022, pp. 2015-2025. ISBN 9781450390965 (doi: 10.1145/3485447.3512202)

Liang, S., Luo, Y. and Meng, Z. (2022) Profiling users for question answering communities via flow-based constrained co-embedding model. ACM Transactions on Information Systems, 40(2), 34. (doi: 10.1145/3470565)

2021

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2021) Variational Bayesian representation learning for grocery recommendation. Information Retrieval, 24(4-5), pp. 347-369. (doi: 10.1007/s10791-021-09397-1)

2020

Meng, Z. , Mccreadie, R. , Macdonald, C. and Ounis, I. (2020) Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 681-686. ISBN 9781450375832 (doi: 10.1145/3383313.3418479)

Meng, Z. et al. (2020) BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 588-590. ISBN 9781450375832 (doi: 10.1145/3383313.3411524)

Huang, H., Meng, Z. and Liang, S. (2020) Recurrent neural variational model for follower-based influence maximization. Information Sciences, 528, pp. 280-293. (doi: 10.1016/j.ins.2020.04.023)

Liu, S., Ounis, I. , Macdonald, C. and Meng, Z. (2020) A Heterogeneous Graph Neural Model for Cold-Start Recommendation. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, pp. 2029-2032. ISBN 9781450380164 (doi: 10.1145/3397271.3401252)

2019

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2019) Variational Bayesian Context-aware Representation for Grocery Recommendation. In: 13th ACM Conference on Recommender Systems (RecSys19) - CARS 2019 Workshop, Copenhagen, Denmark, 16-20 Sept 2019,

Meng, Z. , Liang, S., Fang, J. and Xiao, T. (2019) Semi-supervisedly Co-embedding Attributed Networks. In: Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 08-14 Dec 2019,

This list was generated on Sun Nov 27 03:25:01 2022 GMT.
Number of items: 15.

Articles

Liu, S., Meng, Z. , Macdonald, C. and Ounis, I. (2022) Graph neural pre-training for recommendation with side information. ACM Transactions on Information Systems, (Accepted for Publication)

Wang, Y., Chen, X., Fang, J., Meng, Z. and Liang, S. (2022) Enhancing conversational recommendation systems with representation fusion. ACM Transactions on the Web, (Accepted for Publication)

Meng, Z. et al. (2022) BioCaster in 2021: automatic disease outbreaks detection from global news media. Bioinformatics, 38(18), pp. 4446-4448. (doi: 10.1093/bioinformatics/btac497) (PMID:35900173) (PMCID:PMC9477518)

Tang, S., Meng, Z. and Liang, S. (2022) Dynamic co-embedding model for temporal attributed networks. IEEE Transactions on Neural Networks and Learning Systems, (doi: 10.1109/TNNLS.2022.3193564) (Early Online Publication)

Liang, S., Luo, Y. and Meng, Z. (2022) Profiling users for question answering communities via flow-based constrained co-embedding model. ACM Transactions on Information Systems, 40(2), 34. (doi: 10.1145/3470565)

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2021) Variational Bayesian representation learning for grocery recommendation. Information Retrieval, 24(4-5), pp. 347-369. (doi: 10.1007/s10791-021-09397-1)

Huang, H., Meng, Z. and Liang, S. (2020) Recurrent neural variational model for follower-based influence maximization. Information Sciences, 528, pp. 280-293. (doi: 10.1016/j.ins.2020.04.023)

Conference Proceedings

Chen, G., Liu, F., Meng, Z. and Liang, S. (2022) Revisiting Parameter-Efficient Tuning: Are We Really There Yet? In: 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, 7-11 Dec 2022, (Accepted for Publication)

Meng, Z. , Liu, F., Shareghi, E., Su, Y., Collins, C. and Collier, N. (2022) Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models. In: 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022), Dublin, Ireland, 22-27 May 2022, pp. 4798-4810.

Wang, Y., Xin, X., Meng, Z. , Jose, J. M. , Feng, F. and He, X. (2022) Learning Robust Recommenders Through Cross-Model Agreement. In: ACM Web Conference 2022, Lyon, France, 25-29 April 2022, pp. 2015-2025. ISBN 9781450390965 (doi: 10.1145/3485447.3512202)

Meng, Z. , Mccreadie, R. , Macdonald, C. and Ounis, I. (2020) Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 681-686. ISBN 9781450375832 (doi: 10.1145/3383313.3418479)

Meng, Z. et al. (2020) BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 588-590. ISBN 9781450375832 (doi: 10.1145/3383313.3411524)

Liu, S., Ounis, I. , Macdonald, C. and Meng, Z. (2020) A Heterogeneous Graph Neural Model for Cold-Start Recommendation. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, pp. 2029-2032. ISBN 9781450380164 (doi: 10.1145/3397271.3401252)

Meng, Z. , McCreadie, R. , Macdonald, C. and Ounis, I. (2019) Variational Bayesian Context-aware Representation for Grocery Recommendation. In: 13th ACM Conference on Recommender Systems (RecSys19) - CARS 2019 Workshop, Copenhagen, Denmark, 16-20 Sept 2019,

Meng, Z. , Liang, S., Fang, J. and Xiao, T. (2019) Semi-supervisedly Co-embedding Attributed Networks. In: Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 08-14 Dec 2019,

This list was generated on Sun Nov 27 03:25:01 2022 GMT.

Teaching

  • Recommender Systems (H) (COMPSCI4075(21-22))
  • Recommender Systems (M) (COMPSCI5091(21-22))
  • Introduction to Data Science and Systems (M) (COMPSCI5089(22-23))