Fetching the paper…
Reading the bibliography…
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inadequate collaborative signals, weak latent representations, and noisy data.
Stochastic differential equations
Nicolaas G Van Kampen. 1976 · 1976
Earlier work this paper cites.
E-commerce recommendation applications
J Ben Schafer, Joseph A Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning . 129–136
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web . 661–670
Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010 · 2010
Earlier work this paper cites.
Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, 208–214
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire. 2011 · 2011
Earlier work this paper cites.
Novelty and diversity in top-n recommendation–analysis and evaluation
Neil Hurley and Mi Zhang. 2011 · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent. 2011 · 2011
Earlier work this paper cites.
A survey of music recommendation systems and future perspectives. In 9th international symposium on computer music modeling and retrieval , Vol. 4. 395–410
Yading Song, Simon Dixon, and Marcus Pearce. 2012 · 2012
Earlier work this paper cites.
Generative Adversarial Nets. In NeurIPS , Vol. 27
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Differential privacy and machine learning: a survey and review
Zhanglong Ji, Zachary C Lipton, and Charles Elkan. 2014 · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning . PMLR, 1278–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
The truncated Euler–Maruyama method for stochastic differential equations
Xuerong Mao. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Numerical methods for ordinary differential equations
John Charles Butcher. 2016 · 2016
Earlier work this paper cites.
Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 549–558
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016 · 2016
Earlier work this paper cites.
Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the eleventh ACM conference on recommender systems . 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
Earlier work this paper cites.
Real-time bidding by reinforcement learning in display advertising. In Proceedings of the tenth ACM international conference on web search and data mining . 661–670
Han Cai, Kan Ren, Weinan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, and Defeng Guo. 2017 · 2017
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Visually-aware fashion recommendation and design with generative image models. In 2017 IEEE international conference on data mining (ICDM) . IEEE, 207–216
Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Earlier work this paper cites.
Compatibility family learning for item recommendation and generation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, and Min Sun. 2018 · 2018
Earlier work this paper cites.
Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management . 417–426
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018 · 2018
Earlier work this paper cites.
From recommendation to generation: A novel fashion clothing advising framework. In 2018 7th International Conference on Digital Home (ICDH) . IEEE, 180–186
Zilin Yang, Zhuo Su, Yang Yang, and Ge Lin. 2018 · 2018
Earlier work this paper cites.
Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019 · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks. In ICCV . 4401–4410
Tero Karras, Samuli Laine, and Timo Aila. 2019 · 2019
Earlier work this paper cites.
cGAN: Complementary Fashion Item Recommendation
Sudhir Kumar and Mithun Das Gupta. 2019 · 2019
Earlier work this paper cites.
Generative Modeling by Estimating Gradients of the Data Distribution. In NeurIPS , Vol. 32
Yang Song and Stefano Ermon. 2019 · 2019
Earlier work this paper cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
A review of movie recommendation system: Limitations, Survey and Challenges
Mahesh Goyani and Neha Chaurasiya. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2020 · 2020
Earlier work this paper cites.
A survey of autoencoder-based recommender systems
Guijuan Zhang, Yang Liu, and Xiaoning Jin. 2020b · 2020
Earlier work this paper cites.
Explainable recommendation: A survey and new perspectives
Yongfeng Zhang, Xu Chen, et al · 2020
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. 2021 · 2021
Earlier work this paper cites.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon. 2021 · 2021
Earlier work this paper cites.
An adversarial imitation click model for information retrieval. In Proceedings of the Web Conference 2021 . 1809–1820
Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, and Yong Yu. 2021 · 2021
Earlier work this paper cites.
A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2021 · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
Earlier work this paper cites.
Modeling sequences as distributions with uncertainty for sequential recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management . 3019–3023
Ziwei Fan, Zhiwei Liu, Shen Wang, Lei Zheng, and Philip S Yu. 2021 · 2021
Earlier work this paper cites.
Ad headline generation using self-critical masked language model. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers . 263–271
Yashal Shakti Kanungo, Sumit Negi, and Aruna Rajan. 2021 · 2021
Earlier work this paper cites.
Nu-wave: A diffusion probabilistic model for neural audio upsampling
Junhyeok Lee and Seungu Han. 2021 · 2021
Earlier work this paper cites.
A survey on federated learning systems: Vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. 2021 · 2021
Earlier work this paper cites.
A Graph-Enhanced Click Model for Web Search. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1259–1268
Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Shuai Li, Ruiming Tang, Xiuqiang He, Jianye Hao, and Yong Yu. 2021 · 2021
Earlier work this paper cites.
Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman. 2021 · 2021
Earlier work this paper cites.
Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1784–1793
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021 · 2021
Earlier work this paper cites.
Learning causal explanations for recommendation. In The 1st International Workshop on Causality in Search and Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, and Yongfeng Zhang. 2021 · 2021
Earlier work this paper cites.
Instability and local minima in GAN training with kernel discriminators
Evan Becker, Parthe Pandit, Sundeep Rangan, and Alyson K Fletcher. 2022 · 2022
Earlier work this paper cites.
On investigating the conservative property of score-based generative models
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, and Chun-Yi Lee. 2022 · 2022
Earlier work this paper cites.
Re-imagen: Retrieval-augmented text-to-image generator
Wenhu Chen, Hexiang Hu, Chitwan Saharia, and William W Cohen. 2022 · 2022
Cited alongside, same era.
Riemannian score-based generative modelling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet. 2022 · 2022
Cited alongside, same era.
Field-aware variational autoencoders for billion-scale user representation learning. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) . IEEE, 3413–3425
Ge Fan, Chaoyun Zhang, Junyang Chen, Baopu Li, Zenglin Xu, Yingjie Li, Luyu Peng, and Zhiguo Gong. 2022b · 2022
Cited alongside, same era.
Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. 2022 · 2022
Cited alongside, same era.
How deep learning sees the world: A survey on adversarial attacks & defenses
Joana C Costa, Tiago Roxo, Hugo Proença, and Pedro RM Inácio. 2024 · 2024
Closest in time.
Diffusion-based Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, and Chen Ma. 2024 · 2024
Closest in time.
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce
Ádám Tibor Czapp, Mátyás Jani, Bálint Domián, and Balázs Hidasi. 2024 · 2024
Closest in time.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, and Silvia Milano. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonathan Ho and Tim Salimans. 2022 · 2022
Cited alongside, same era.
Global context with discrete diffusion in vector quantised modelling for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11502–11511
Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, and Ponnuthurai N Suganthan. 2022 · 2022
Cited alongside, same era.
Causal machine learning: A survey and open problems
Jean Kaddour, Aengus Lynch, Qi Liu, Matt J Kusner, and Ricardo Silva. 2022 · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 11461–11471
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
Cited alongside, same era.
Scalable Diffusion Models with Transformers
William Peebles and Saining Xie. 2022 · 2022
Cited alongside, same era.
Text-guided synthesis of artistic images with retrieval-augmented diffusion models
Robin Rombach, Andreas Blattmann, and Björn Ommer. 2022a · 2022
Cited alongside, same era.
Structure-based drug design with equivariant diffusion models
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, et al · 2022
Cited alongside, same era.
Hao Dong, Haochen Liang, Jing Yu, and Keke Gai. 2024 · 2024
Closest in time.
Kounianhua Du, Jizheng Chen, Jianghao Lin, Yunjia Xi, Hangyu Wang, Xinyi Dai, Bo Chen, Ruiming Tang, and Weinan Zhang. 2024 · 2024
Closest in time.
Diffusion Models and Representation Learning: A Survey
Michael Fuest, Pingchuan Ma, Ming Gui, Johannes S Fischer, Vincent Tao Hu, and Bjorn Ommer. 2024 · 2024
Closest in time.
AIGB: Generative Auto-bidding via Diffusion Modeling
Jiayan Guo, Yusen Huo, Zhilin Zhang, Tianyu Wang, Chuan Yu, Jian Xu, Yan Zhang, and Bo Zheng. 2024 · 2024
Closest in time.
Xin He, Wenqi Fan, Ruobing Wang, Yili Wang, Ying Wang, Shirui Pan, and Xin Wang. 2024a · 2024
Closest in time.
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1360–1369
Yu Hou, Jin-Duk Park, and Won-Yong Shin. 2024 · 2024
Closest in time.
DiffMM: Multi-Modal Diffusion Model for Recommendation
Yangqin Jiang, Lianghao Xia, Wei Wei, Da Luo, Kangyi Lin, and Chao Huang. 2024a · 2024
Closest in time.
DGRM: Diffusion-GAN recommendation model to alleviate the mode collapse problem in sparse environments
Deng Jiangzhou, Wang Songli, Ye Jianmei, Ji Lianghao, and Wang Yong. 2024 · 2024
Closest in time.
Predict, refine, synthesize: Self-guiding diffusion models for probabilistic time series forecasting
Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, and Yuyang Bernie Wang. 2024 · 2024
Closest in time.
Voicebox: Text-guided multilingual universal speech generation at scale
Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, et al · 2024
Closest in time.
Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, and Noseong Park. 2024 · 2024
Closest in time.
Multi-Interest Network with Simple Diffusion for Multi-Behavior Sequential Recommendation. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) . SIAM, 734–742
Qingfeng Li, Huifang Ma, Wangyu Jin, Yugang Ji, and Zhixin Li. 2024e · 2024
Closest in time.
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
Wuchao Li, Rui Huang, Haijun Zhao, Chi Liu, Kai Zheng, Qi Liu, Na Mou, Guorui Zhou, Defu Lian, Yang Song, et al · 2024
Closest in time.
A Survey of Generative Search and Recommendation in the Era of Large Language Models
Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024d · 2024
Closest in time.
RecDiff: Diffusion Model for Social Recommendation
Zongwei Li, Lianghao Xia, and Chao Huang. 2024f · 2024
Closest in time.
A Survey on Variational Autoencoders in Recommender Systems
Shangsong Liang, Zhou Pan, wei liu, Jian Yin, and Maarten de Rijke. 2024 · 2024
Closest in time.
Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
Derek Lilienthal, Paul Mello, Magdalini Eirinaki, and Stas Tiomkin. 2024 · 2024
Closest in time.
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong Liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, and Weinan Zhang. 2024b · 2024
Closest in time.
Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation. In Proceedings of the ACM on Web Conference 2024 . 3497–3508
Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024c · 2024
Closest in time.
Discrete conditional diffusion for reranking in recommendation. In Companion Proceedings of the ACM on Web Conference 2024 . 161–169
Xiao Lin, Xiaokai Chen, Chenyang Wang, Hantao Shu, Linfeng Song, Biao Li, and Peng Jiang. 2024a · 2024
Closest in time.
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Chengkai Liu, Jianghao Lin, Hanzhou Liu, Jianling Wang, and James Caverlee. 2024a · 2024
Closest in time.
Mamba4rec: Towards efficient sequential recommendation with selective state space models
Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, and James Caverlee. 2024b · 2024
Closest in time.
ToDA: Target-oriented Diffusion Attacker against Recommendation System
Xiaohao Liu, Zhulin Tao, Ting Jiang, He Chang, Yunshan Ma, and Xianglin Huang. 2024c · 2024
Closest in time.
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations
Jing Long, Guanhua Ye, Tong Chen, Yang Wang, Meng Wang, and Hongzhi Yin. 2024 · 2024
Closest in time.
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models
Bingqi Ma, Zhuofan Zong, Guanglu Song, Hongsheng Li, and Yu Liu. 2024e · 2024
Closest in time.
Multimodal Conditioned Diffusion Model for Recommendation. In Companion Proceedings of the ACM on Web Conference 2024 . 1733–1740
Haokai Ma, Yimeng Yang, Lei Meng, Ruobing Xie, and Xiangxu Meng. 2024d · 2024
Closest in time.
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Ludovico Boratto, Fragkiskos D Malliaros, Mirko Marras, and Ernesto William De Luca. 2024 · 2024
Closest in time.
A watermark-conditioned diffusion model for ip protection
Rui Min, Sen Li, Hongyang Chen, and Minhao Cheng. 2024 · 2024
Closest in time.
MMCRec: Towards Multi-modal Generative AI in Conversational Recommendation. In European Conference on Information Retrieval . Springer, 316–325
Tendai Mukande, Esraa Ali, Annalina Caputo, Ruihai Dong, and Noel E O’Connor. 2024 · 2024
Closest in time.
Diffusion Recommendation with Implicit Sequence Influence. In Companion Proceedings of the ACM on Web Conference 2024 . 1719–1725
Yong Niu, Xing Xing, Zhichun Jia, Ruidi Liu, Mindong Xin, and Jianfu Cui. 2024 · 2024
Closest in time.
PMG: Personalized Multimodal Generation with Large Language Models. In Proceedings of the ACM on Web Conference 2024 . 3833–3843
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, and Xi Xiao. 2024 · 2024
Closest in time.
Diffusion Model for Slate Recommendation
Federico Tomasi, Francesco Fabbri, Mounia Lalmas, and Zhenwen Dai. 2024 · 2024
Closest in time.
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
Mario Villaizán-Vallelado, Matteo Salvatori, Carlos Segura, and Ioannis Arapakis. 2024 · 2024
Closest in time.
Patch diffusion: Faster and more data-efficient training of diffusion models
Zhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Zhangyang Wang, Weizhu Chen, Mingyuan Zhou, et al · 2024
Closest in time.
DSDRec: Next POI recommendation using deep semantic extraction and diffusion model
Ziwei Wang, Jun Zeng, Lin Zhong, Ling Liu, Min Gao, and Junhao Wen. 2024e · 2024
Closest in time.
MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, and Yong Yu. 2024 · 2024
Closest in time.
Survey for Landing Generative AI in Social and E-commerce Recsys–the Industry Perspectives
Da Xu, Danqing Zhang, Guangyu Yang, Bo Yang, Shuyuan Xu, Lingling Zheng, and Cindy Liang. 2024b · 2024
Closest in time.
Diffusion Cross-domain Recommendation
Yuner Xuan. 2024 · 2024
Closest in time.
A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model. In Companion Proceedings of the ACM on Web Conference 2024 . 180–189
Hao Yang, Jianxin Yuan, Shuai Yang, Linhe Xu, Shuo Yuan, and Yifan Zeng. 2024d · 2024
Closest in time.
A survey on diffusion models for time series and spatio-temporal data
Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, et al · 2024
Closest in time.
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Zeyu Yang, Peikun Guo, Khadija Zanna, and Akane Sano. 2024a · 2024
Closest in time.
Generate what you prefer: Reshaping sequential recommendation via guided diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, and Xiangnan He. 2024c · 2024
Closest in time.
A Directional Diffusion Graph Transformer for Recommendation
Zixuan Yi, Xi Wang, and Iadh Ounis. 2024 · 2024
Closest in time.
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang, Pengxiang Lan, Yuting Liu, Jianzhe Zhao, Guibing Guo, and Xingwei Wang. 2024b · 2024
Closest in time.
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. 2024 · 2024
Closest in time.
Graph Signal Diffusion Model for Collaborative Filtering. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1380–1390
Yunqin Zhu, Chao Wang, Qi Zhang, and Hui Xiong. 2024 · 2024
Closest in time.
Diff-DGMN: A Diffusion-Based Dual Graph Multi-Attention Network for POI Recommendation
Jiankai Zuo and Yaying Zhang. 2024 · 2024
Closest in time.
Sequential recommendation via stochastic self-attention. In Proceedings of the ACM web conference 2022 . 2036–2047
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022a · 2047
Closest in time.