| Relational collaborative filtering: Modeling multiple item relations for recommendation X Xin, X He, Y Zhang, Y Zhang, J Jose Proceedings of the 42nd international ACM SIGIR conference on research and …, 2019 | 94 | 2019 |
| Self-supervised reinforcement learning for recommender systems X Xin, A Karatzoglou, I Arapakis, JM Jose Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 75 | 2020 |
| CFM: Convolutional Factorization Machines for Context-Aware Recommendation. X Xin, B Chen, X He, D Wang, Y Ding, JM Jose IJCAI 19, 3926-3932, 2019 | 72 | 2019 |
| fBGD: Learning embeddings from positive unlabeled data with BGD F Yuan, X Xin, X He, G Guo, W Zhang, C Tat-Seng, JM Jose | 31 | 2018 |
| AutoDebias: Learning to debias for recommendation J Chen, H Dong, Y Qiu, X He, X Xin, L Chen, G Lin, K Yang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 27 | 2021 |
| Should graph convolution trust neighbors? a simple causal inference method F Feng, W Huang, X He, X Xin, Q Wang, TS Chua Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 22 | 2021 |
| Batch IS NOT heavy: LearningWord representations from all samples X Xin, F Yuan, X He, JM Jose | 16 | 2018 |
| SCFM: Social and crowdsourcing factorization machines for recommendation Y Ding, D Wang, X Xin, G Li, D Sun, X Zeng, R Ranjan Applied Soft Computing 66, 548-556, 2018 | 9 | 2018 |
| TGCN: Tag graph convolutional network for tag-aware recommendation B Chen, W Guo, R Tang, X Xin, Y Ding, X He, D Wang Proceedings of the 29th ACM International Conference on Information …, 2020 | 6 | 2020 |
| FHSM: factored hybrid similarity methods for top-n recommender systems X Xin, D Wang, Y Ding, C Lini Asia-Pacific Web Conference, 98-110, 2016 | 6 | 2016 |
| Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training M Cheng, F Yuan, Q Liu, X Xin, E Chen 2021 IEEE International Conference on Data Mining (ICDM), 51-60, 2021 | 4 | 2021 |
| Graph Convolutional Embeddings for Recommender Systems P Gómez Duran, A Karatzoglou, J Vitriŕ, X Xin, I Arapakis arXiv e-prints, arXiv: 2103.03587, 2021 | 4* | 2021 |
| AIRec: Attentive intersection model for tag-aware recommendation B Chen, Y Ding, X Xin, Y Li, Y Wang, D Wang Neurocomputing 421, 105-114, 2021 | 4 | 2021 |
| Graph highway networks X Xin, A Karatzoglou, I Arapakis, JM Jose arXiv preprint arXiv:2004.04635, 2020 | 4 | 2020 |
| Exploiting long‐term and short‐term preferences and RFID trajectories in shop recommendation Y Ding, D Wang, G Li, D Sun, X Xin, S Qian Software: Practice and Experience 47 (6), 849-865, 2017 | 4 | 2017 |
| Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning D Stamenkovic, A Karatzoglou, I Arapakis, X Xin, K Katevas Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 2 | 2022 |
| Variational Reasoning about User Preferences for Conversational Recommendation Z Ren, Z Tian, D Li, P Ren, L Yang, X Xin, H Liang, M de Rijke, Z Chen | 2 | 2022 |
| Decomposed collaborative filtering: modeling explicit and implicit factors for recommender systems H Chen, X Xin, D Wang, Y Ding Proceedings of the 14th ACM international conference on web search and data …, 2021 | 2 | 2021 |
| Learning Robust Recommenders through Cross-Model Agreement Y Wang, X Xin, Z Meng, JM Jose, F Feng, X He Proceedings of the ACM Web Conference 2022, 2015-2025, 2022 | 1 | 2022 |
| Supervised Advantage Actor-Critic for Recommender Systems X Xin, A Karatzoglou, I Arapakis, JM Jose Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 1 | 2022 |
Joemon JoseProfessor of Computer Science, University of GlasgowVerified email at glasgow.ac.uk
