Number of items: 16.
2023
Liu, S., Meng, Z. , Macdonald, C. and Ounis, I.
(2023)
Graph neural pre-training for recommendation with side information.
ACM Transactions on Information Systems, 41(3),
74.
(doi: 10.1145/3568953)
Wang, Y., Chen, X., Fang, J., Meng, Z. and Liang, S.
(2023)
Enhancing conversational recommendation systems with representation fusion.
ACM Transactions on the Web, 17(1),
6.
(doi: 10.1145/3577034)
Yuan, Z., Hu, S., Vulic, I., Korhonen, A. and Meng, Z.
(2023)
Can Pretrained Language Models (Yet) Reason Deductively?
In: 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), Dubrovnik, Croatia, 2-6 May 2023,
(Accepted for Publication)
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)
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 Sat Apr 1 19:56:20 2023 BST.