Nanyang Technological University, Singapore
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Geciteerd door
Recurrent knowledge graph embedding for effective recommendation
Z Sun, J Yang, J Zhang, A Bozzon, LK Huang, C Xu
Proceedings of the 12th ACM conference on recommender systems, 297-305, 2018
Librec: A java library for recommender systems.
G Guo, J Zhang, Z Sun, N Yorke-Smith
UMAP workshops 4, 38-45, 2015
Research commentary on recommendations with side information: A survey and research directions
Z Sun, Q Guo, J Yang, H Fang, G Guo, J Zhang, R Burke
Electronic Commerce Research and Applications 37, 100879, 2019
Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison
Z Sun, D Yu, H Fang, J Yang, X Qu, J Zhang, C Geng
Proceedings of the 14th ACM Conference on Recommender Systems, 23-32, 2020
BPRH: Bayesian personalized ranking for heterogeneous implicit feedback
H Qiu, Y Liu, G Guo, Z Sun, J Zhang, HT Nguyen
Information Sciences 453, 80-98, 2018
An attentional recurrent neural network for personalized next location recommendation
Q Guo, Z Sun, J Zhang, YL Theng
Proceedings of the AAAI Conference on artificial intelligence 34 (01), 83-90, 2020
Interacting attention-gated recurrent networks for recommendation
W Pei, J Yang, Z Sun, J Zhang, A Bozzon, DMJ Tax
Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017
Attentive Knowledge Graph Embedding for Personalized Recommendation
X Sha, Z Sun, J Zhang
https://arxiv.org/abs/1910.08288, 2019
Modeling hierarchical category transition for next POI recommendation with uncertain check-ins
L Zhang, Z Sun, J Zhang, H Kloeden, F Klanner
Information Sciences 515, 169-190, 2020
An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins
L Zhang, Z Sun, J Zhang, Y Lei, C Li, Z Wu, H Kloeden, F Klanner
IJCAI 301 (985), 13954, 2020
Learning hierarchical feature influence for recommendation by recursive regularization
J Yang, Z Sun, A Bozzon, J Zhang
Proceedings of the 10th ACM Conference on Recommender Systems, 51-58, 2016
Minimalistic attacks: How little it takes to fool deep reinforcement learning policies
X Qu, Z Sun, YS Ong, A Gupta, P Wei
IEEE Transactions on Cognitive and Developmental Systems 13 (4), 806-817, 2020
MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation.
Z Sun, J Yang, J Zhang, A Bozzon, Y Chen, C Xu
IJCAI, 2807-2813, 2017
Exploiting both vertical and horizontal dimensions of feature hierarchy for effective recommendation
Z Sun, J Yang, J Zhang, A Bozzon
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
Aspect-aware point-of-interest recommendation with geo-social influence
Q Guo, Z Sun, J Zhang, Q Chen, YL Theng
Adjunct Publication of the 25th Conference on User Modeling, Adaptation and …, 2017
Exploiting implicit item relationships for recommender systems
Z Sun, G Guo, J Zhang
User Modeling, Adaptation and Personalization: 23rd International Conference …, 2015
Tbpr: Trinity preference based bayesian personalized ranking for multivariate implicit feedback
H Qiu, G Guo, J Zhang, Z Sun, HT Nguyen, Y Liu
Proceedings of the 2016 Conference on User Modeling Adaptation and …, 2016
Exploiting side information for recommendation
Q Guo, Z Sun, YL Theng
Web Engineering: 19th International Conference, ICWE 2019, Daejeon, South …, 2019
Multi-facet user preference learning for fine-grained item recommendation
X Zhou, G Guo, Z Sun, Y Liu
Neurocomputing 385, 258-268, 2020
Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data.
Y Sun, B Wang, Z Sun, X Yang
IJCAI, 1579-1585, 2021
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