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  • Katz O, Barkan O and Koenigstein N. (2024). Personalized Cadence Awareness for Next Basket Recommendation. ACM Transactions on Recommender Systems. 3:1. (1-23). Online publication date: 31-Mar-2025.

    https://doi.org/10.1145/3652863

  • Lan W, Zhou G, Chen Q, Wang W, Pan S, Pan Y and Zhang S. (2024). Contrastive Clustering Learning for Multi-Behavior Recommendation. ACM Transactions on Information Systems. 43:1. (1-23). Online publication date: 31-Jan-2025.

    https://doi.org/10.1145/3698192

  • Jansen L, Bennin K, van Kleef E and Van Loo E. (2024). Online grocery shopping recommender systems. Computers in Human Behavior. 159:C. Online publication date: 1-Oct-2024.

    https://doi.org/10.1016/j.chb.2024.108336

  • Ma C, Ren Y, Castells P and Sanderson M. Temporal Conformity-aware Hawkes Graph Network for Recommendations. Proceedings of the ACM Web Conference 2024. (3185-3194).

    https://doi.org/10.1145/3589334.3645354

  • Guo L, Zhang J, Tang L, Chen T, Zhu L and Yin H. Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation. IEEE Transactions on Neural Networks and Learning Systems. 10.1109/TNNLS.2022.3201533. 35:3. (4002-4016).

    https://ieeexplore.ieee.org/document/9881215/

  • Zhang K, Chu D, Tu Z, Liu X and Zhang B. (2024). LSTM-UBI: a user behavior inertia based recommendation method. Multimedia Tools and Applications. 10.1007/s11042-024-18256-2. 83:27. (69227-69248).

    https://link.springer.com/10.1007/s11042-024-18256-2

  • Wu W, Ghazali M and Hazlin Huspi S. A Review of User Profiling Based on Social Networks. IEEE Access. 10.1109/ACCESS.2024.3430987. 12. (122642-122670).

    https://ieeexplore.ieee.org/document/10604809/

  • Koh J and Chen C. (2024). Category-Aware Sequential Recommendation with Time Intervals of Purchases. Database and Expert Systems Applications. 10.1007/978-3-031-68309-1_21. (249-257).

    https://link.springer.com/10.1007/978-3-031-68309-1_21

  • Shih C, Lu C and Hwang I. (2022). Cross-domain incremental recommendation system based on meta learning. Journal of Ambient Intelligence and Humanized Computing. 10.1007/s12652-022-03911-z. 14:12. (16563-16574). Online publication date: 1-Dec-2023.

    https://link.springer.com/10.1007/s12652-022-03911-z

  • Wang S, Wang Y, Hu L, Zhang X, Zhang Q, Sheng Q, Orgun M, Cao L and Lian D. Modeling User Demand Evolution for Next-Basket Prediction. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3231018. 35:11. (11585-11598).

    https://ieeexplore.ieee.org/document/9996156/

  • Gao X, Ma Z, Cui J, Xia X and Xu C. Hierarchical Category-Enhanced Prototype Learning for Imbalanced Temporal Recommendation. Proceedings of the 31st ACM International Conference on Multimedia. (6181-6189).

    https://doi.org/10.1145/3581783.3613829

  • Bian Q, Xu J, Fang H and Ke Y. CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (120-130).

    https://doi.org/10.1145/3583780.3615512

  • Ding H, Kveton B, Ma Y, Park Y, Kini V, Gu Y, Divvela R, Wang F, Deoras A and Wang H. Trending Now: Modeling Trend Recommendations. Proceedings of the 17th ACM Conference on Recommender Systems. (294-305).

    https://doi.org/10.1145/3604915.3608810

  • Zheng L, Chai H, Chen X, Jin J, Zhang W, Yu Y, Guo X, Ge C and Feng Z. (2023). Search-based Time-Aware Graph-Enhanced Recommendation with Sequential Behavior Data. ACM Transactions on Recommender Systems. 10.1145/3605356.

    https://dl.acm.org/doi/10.1145/3605356

  • Zhou W, Kang Z, Tian L and Su Y. (2023). Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts. Information Sciences. 10.1016/j.ins.2023.119318. (119318). Online publication date: 1-Jun-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0020025523009039

  • Liu H, Cai K, Li P, Qian C, Zhao P and Wu X. (2023). REDRL: A review-enhanced Deep Reinforcement Learning model for interactive recommendation. Expert Systems with Applications. 10.1016/j.eswa.2022.118926. 213. (118926). Online publication date: 1-Mar-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0957417422019443

  • Bogina V, Kuflik T, Jannach D, Bielikova M, Kompan M and Trattner C. (2022). Considering temporal aspects in recommender systems: a survey. User Modeling and User-Adapted Interaction. 10.1007/s11257-022-09335-w. 33:1. (81-119). Online publication date: 1-Mar-2023.

    https://link.springer.com/10.1007/s11257-022-09335-w

  • Li Z and Sun M. (2023). Sparse Transformer Hawkes Process for Long Event Sequences. Machine Learning and Knowledge Discovery in Databases: Research Track. 10.1007/978-3-031-43424-2_11. (172-188).

    https://link.springer.com/10.1007/978-3-031-43424-2_11

  • Wang D, Zhang X, Xiang Z, Yu D, Xu G and Deng S. Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention. IEEE Transactions on Cybernetics. 10.1109/TCYB.2021.3077361. 52:11. (11893-11905).

    https://ieeexplore.ieee.org/document/9447892/

  • Ji Y, Sun A, Zhang J and Li C. (2022). A Critical Study on Data Leakage in Recommender System Offline Evaluation. ACM Transactions on Information Systems. 10.1145/3569930.

    https://dl.acm.org/doi/10.1145/3569930

  • Guo J, Zhang P, Li C, Xie X, Zhang Y and Kim S. Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (624-634).

    https://doi.org/10.1145/3511808.3557314

  • Katz O, Barkan O, Koenigstein N and Zabari N. Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation. Proceedings of the 16th ACM Conference on Recommender Systems. (316-326).

    https://doi.org/10.1145/3523227.3546763

  • Zhu W, Xie Y, Huang Q, Zheng Z, Fang X, Huang Y and Sun W. (2022). Graph Transformer Collaborative Filtering Method for Multi-Behavior Recommendations. Mathematics. 10.3390/math10162956. 10:16. (2956).

    https://www.mdpi.com/2227-7390/10/16/2956

  • Xi Y, Liu W, Zhu J, Zhao X, Dai X, Tang R, Zhang W, Zhang R and Yu Y. Multi-Level Interaction Reranking with User Behavior History. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1336-1346).

    https://doi.org/10.1145/3477495.3532026

  • Wang X, Li Q, Yu D and Xu G. Off-policy Learning over Heterogeneous Information for Recommendation. Proceedings of the ACM Web Conference 2022. (2348-2359).

    https://doi.org/10.1145/3485447.3512072

  • Bai T, Xiao Y, Wu B, Yang G, Yu H and Nie J. A Contrastive Sharing Model for Multi-Task Recommendation. Proceedings of the ACM Web Conference 2022. (3239-3247).

    https://doi.org/10.1145/3485447.3512043

  • Jiang Y, Liu G, Wu J and Lin H. Telecom Fraud Detection via Hawkes-enhanced Sequence Model. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3150803. (1-1).

    https://ieeexplore.ieee.org/document/9712421/

  • Wu L, He X, Wang X, Zhang K and Wang M. A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3145690. (1-1).

    https://ieeexplore.ieee.org/document/9693280/

  • Li W, Yang P, Wang X and Xiao Y. (2022). Sequence Recommendation Model with Double-Layer Attention Net. Database and Expert Systems Applications. 10.1007/978-3-031-12426-6_7. (84-96).

    https://link.springer.com/10.1007/978-3-031-12426-6_7

  • Li Y, Ding Y, Chen B, Xin X, Wang Y, Shi Y, Tang R and Wang D. Extracting Attentive Social Temporal Excitation for Sequential Recommendation. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. (998-1007).

    https://doi.org/10.1145/3459637.3482257

  • Zhao P, Luo C, Zhou C, Qiao B, He J, Zhang L and Lin Q. RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate Prediction. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2268-2272).

    https://doi.org/10.1145/3404835.3463012

  • Bai T, Zhang Y, Wu B and Nie J. (2020). Temporal Graph Neural Networks for Social Recommendation 2020 IEEE International Conference on Big Data (Big Data). 10.1109/BigData50022.2020.9378444. 978-1-7281-6251-5. (898-903).

    https://ieeexplore.ieee.org/document/9378444/

  • Zou L, Xia L, Gu Y, Zhao X, Liu W, Huang J and Yin D. Neural Interactive Collaborative Filtering. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. (749-758).

    https://doi.org/10.1145/3397271.3401181

  • Gu Y, Ding Z, Wang S and Yin D. Hierarchical User Profiling for E-commerce Recommender Systems. Proceedings of the 13th International Conference on Web Search and Data Mining. (223-231).

    https://doi.org/10.1145/3336191.3371827

  • Zou L, Xia L, Du P, Zhang Z, Bai T, Liu W, Nie J and Yin D. Pseudo Dyna-Q. Proceedings of the 13th International Conference on Web Search and Data Mining. (816-824).

    https://doi.org/10.1145/3336191.3371801