Fetching the paper…
Reading the bibliography…
Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity).
The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval . 335–336
Jaime Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Introduction to stochastic programming
John R Birge and Francois Louveaux. 2011 · 2011
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Session-based Recommendations with Recurrent Neural Networks
B Hidasi. 2015 · 2015
Earlier work this paper cites.
Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems . 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3994–4003
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016 · 2016
Earlier work this paper cites.
Deep multi-task representation learning: A tensor factorisation approach
Yongxin Yang and Timothy Hospedales. 2016 · 2016
Earlier work this paper cites.
Learning multiple tasks with multilinear relationship networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Philip S Yu. 2017 · 2017
Earlier work this paper cites.
An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
Earlier work this paper cites.
Adapting Markov decision process for search result diversification. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval . 535–544
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng. 2017 · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun. 2018 · 2018
Earlier work this paper cites.
Pareto multi-task learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. 2019 · 2019
Earlier work this paper cites.
End-To-End Multi-Task Learning With Attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Shikun Liu, Edward Johns, and Andrew J. Davison. 2019 · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Earlier work this paper cites.
Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization. In International Conference on Machine Learning . PMLR, 6597–6607
Debabrata Mahapatra and Vaibhav Rajan. 2020 · 2020
Earlier work this paper cites.
Denoising Diffusion Implicit Models. In International Conference on Learning Representations
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020 · 2020
Earlier work this paper cites.
The hypervolume indicator: Computational problems and algorithms
Andreia P Guerreiro, Carlos M Fonseca, and Luís Paquete. 2021 · 2021
Earlier work this paper cites.
Parameter prediction for unseen deep architectures
Boris Knyazev, Michal Drozdzal, Graham W Taylor, and Adriana Romero Soriano. 2021 · 2021
Earlier work this paper cites.
Improved denoising diffusion probabilistic models. In International Conference on Machine Learning . PMLR, 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Cited alongside, same era.
Computationally efficient optimization of plackett-luce ranking models for relevance and fairness. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1023–1032
Harrie Oosterhuis. 2021 · 2021
Cited alongside, same era.
Personalized approximate pareto-efficient recommendation. In Proceedings of the Web Conference 2021 . 3839–3849
Ruobing Xie, Yanlei Liu, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. 2021 · 2021
Cited alongside, same era.
Diversification-aware learning to rank using distributed representation. In Proceedings of the Web Conference 2021 . 127–136
Le Yan, Zhen Qin, Rama Kumar Pasumarthi, Xuanhui Wang, and Michael Bendersky. 2021 · 2021
Cited alongside, same era.
A survey on multi-task learning
DiGress: Discrete Denoising diffusion for graph generation. In Proceedings of the 11th International Conference on Learning Representations
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2023 · 2023
Later among the works it cites.
Multi-task deep recommender systems: A survey
Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, and Ruiming Tang. 2023a · 2023
Later among the works it cites.
A survey on the fairness of recommender systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023b · 2023
Later among the works it cites.
Adatask: A task-aware adaptive learning rate approach to multi-task learning. In Proceedings of the AAAI conference on artificial intelligence , Vol. 37. 10745–10753
Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, and Guibing Guo. 2023 · 2023
Later among the works it cites.
Enhancing Sequential Recommendations through Multi-Perspective Reflections and Iteration
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yu Zhang and Qiang Yang. 2021 · 2021
Cited alongside, same era.
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In The Eleventh International Conference on Learning Representations
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2022 · 2022
Cited alongside, same era.
Metabalance: improving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks. In Proceedings of the ACM Web Conference 2022 . 2205–2215
Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, and James Caverlee. 2022 · 2022
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans. 2022 · 2022
Cited alongside, same era.
Multi-objective recommendation: Overview and challenges. In Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022) , Vol. 3268
Dietmar Jannach. 2022 · 2022
Cited alongside, same era.
Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. 2022 · 2022
Cited alongside, same era.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In ICML . PMLR, 16784–16804
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
Cited alongside, same era.
Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Xiao Zhang, Ming He, Jianping Fan, and Jun Xu. 2024b · 2024
Closest in time.
A survey of controllable learning: Methods and applications in information retrieval
Chenglei Shen, Xiao Zhang, Teng Shi, Changshuo Zhang, Guofu Xie, and Jun Xu. 2024 · 2024
Closest in time.
UniSAR: Modeling User Transition Behaviors between Search and Recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1029–1039
Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu, and Yang Song. 2024 · 2024
Closest in time.
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 9002–9010
Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, and Jie Jiang. 2024 · 2024
Closest in time.
Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell, Zhuang Liu, and Yang You. 2024 · 2024
Closest in time.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation. In The Twelfth International Conference on Learning Representations
Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, and Yong Li. 2024 · 2024
Closest in time.
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang, Qi Liu, Ruobing Xie, Jun Xu, and Ji-Rong Wen. 2024d · 2024
Closest in time.
Model-Agnostic Causal Embedding Learning for Counterfactually Group-Fair Recommendation
Xiao Zhang, Teng Shi, Jun Xu, Zhenhua Dong, and Ji-Rong Wen. 2024c · 2024
Closest in time.
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment
Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, and Jun Xu. 2025a · 2025
Closest in time.
Similarity= Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, and Jun Xu. 2025b · 2025
Closest in time.
Uncertainty-aware evidential learning for legal case retrieval with noisy correspondence
Weicong Qin, Weijie Yu, Kepu Zhang, Haiyuan Zhao, Jun Xu, and Ji-Rong Wen. 2025c · 2025
Closest in time.
Unified Generative Search and Recommendation
Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, and Enyun Yu. 2025 · 2025
Closest in time.
Decoding Recommendation Behaviors of In-Context Learning LLMs Through Gradient Descent
Yi Xu, Weicong Qin, Weijie Yu, Ming He, Jianping Fan, and Jun Xu. 2025 · 2025
Closest in time.
Comment Staytime Prediction with LLM-enhanced Comment Understanding. In Companion Proceedings of the ACM on Web Conference 2025 . 586–595
Changshuo Zhang, Zihan Lin, Shukai Liu, Yongqi Liu, and Han Li. 2025a · 2025
Closest in time.
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Changshuo Zhang, Xiao Zhang, Teng Shi, Jun Xu, and Ji-Rong Wen. 2025b · 2025
Closest in time.