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Federated Recommender System Based on Diffusion Augmentation and Guided Denoising
Abstract
Sequential recommender systems often struggle with accurate personalized recommendations due to data sparsity issues. Existing works use variational autoencoders and generative adversarial network methods to enrich sparse data. However, they often overlook diversity in the latent data distribution, hindering the model’s generative capacity. This characteristic of generative methods can introduce additional noise in many cases. Moreover, retaining personalized user preferences through the generation process remains a challenge. This work introduces DGFedRS, a Federated Recommender System Based on Diffusion Augmentation and Guided Denoising, designed to capture the diversity in the latent data distribution while preserving user-specific information and suppressing noise. In particular, we pre-train the diffusion model using the recommender dataset and use a diffusion augmentation strategy to generate interaction sequences, expanding the sparse user-item interactions in the discrete space. To preserve user-specific preferences in the generated interactions, we employ a guided denoising strategy to guide the generation process during reverse diffusion. Subsequently, we design a noise control strategy to reduce the damage to personalized information during the diffusion process. Additionally, a stepwise scheduling strategy is devised to input generated data into the sequential recommender model based on their challenge levels. The success of the DGFedRS approach is demonstrated by thorough experiments conduct on three real-world datasets.
References
[1]
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447 (2023).
[2]
Shuqing Bian, Wayne Xin Zhao, Kun Zhou, Jing Cai, Yancheng He, Cunxiang Yin, and Ji-Rong Wen. 2021. Contrastive curriculum learning for sequential user behavior modeling via data augmentation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3737–3746.
[3]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[4]
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace. 2023. Extracting training data from diffusion models. In 32nd USENIX Security Symposium (USENIX Security 23). 5253–5270.
[5]
Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. 2020. Controllable multi-interest framework for recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2942–2951.
[6]
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In Proceedings of the ACM Web Conference 2022. 2172–2182.
[7]
Jianpeng Cheng, Li Dong, and Mirella Lapata. 2016. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733 (2016).
[8]
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath. 2018. Generative adversarial networks: An overview. IEEE signal processing magazine 35, 1 (2018), 53–65.
[9]
Yizhou Dang, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Xiaoxiao Xu, Qinghui Sun, and Hong Liu. 2023. Uniform sequence better: Time interval aware data augmentation for sequential recommendation. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 4225–4232.
[10]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[11]
Jiasheng Duan, Peng-Fei Zhang, Ruihong Qiu, and Zi Huang. 2023. Long short-term enhanced memory for sequential recommendation. World Wide Web 26, 2 (2023), 561–583.
[12]
Wenqi Fan, Chengyi Liu, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, and Qing Li. 2023. Generative diffusion models on graphs: Methods and applications. arXiv preprint arXiv:2302.02591 (2023).
[13]
Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, and Xing Xie. 2022. Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1599–1610.
[14]
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022. Sequential recommendation via stochastic self-attention. In Proceedings of the ACM Web Conference 2022. 2036–2047.
[15]
Honghao Gao, Yueshen Xu, Yuyu Yin, Weipeng Zhang, Rui Li, and Xinheng Wang. 2019. Context-aware QoS prediction with neural collaborative filtering for Internet-of-Things services. IEEE Internet of Things Journal 7, 5 (2019), 4532–4542.
[16]
Jingtong Gao, Xiangyu Zhao, Muyang Li, Minghao Zhao, Runze Wu, Ruocheng Guo, Yiding Liu, and Dawei Yin. 2023. SMLP4Rec: An Efficient all-MLP Architecture for Sequential Recommendations. ACM Transactions on Information Systems (2023).
[17]
Stephen Grossberg. 2013. Recurrent neural networks. Scholarpedia 8, 2 (2013), 1888.
[18]
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840–6851.
[19]
Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. 2023. Learning vector-quantized item representation for transferable sequential recommenders. In Proceedings of the ACM Web Conference 2023. 1162–1171.
[20]
Jun Hu, Bryan Hooi, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2024. Mgdcf: Distance learning via markov graph diffusion for neural collaborative filtering. IEEE Transactions on Knowledge and Data Engineering (2024).
[21]
Liwei Huang, Yutao Ma, Yanbo Liu, Bohong Danny Du, Shuliang Wang, and Deyi Li. 2023. Position-enhanced and time-aware graph convolutional network for sequential recommendations. ACM Transactions on Information Systems 41, 1 (2023), 1–32.
[22]
Xu Huang, Defu Lian, Jin Chen, Liu Zheng, Xing Xie, and Enhong Chen. 2023. Cooperative Retriever and Ranker in Deep Recommenders. In Proceedings of the ACM Web Conference 2023. 1150–1161.
[23]
Arta Iftikhar, Mustansar Ali Ghazanfar, Mubbashir Ayub, Saad Ali Alahmari, Nadeem Qazi, and Julie Wall. 2024. A reinforcement learning recommender system using bi-clustering and Markov Decision Process. Expert Systems with Applications 237 (2024), 121541.
[24]
Abiodun M Ikotun, Absalom E Ezugwu, Laith Abualigah, Belal Abuhaija, and Jia Heming. 2023. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences 622 (2023), 178–210.
[25]
Renqi Jia, Xu Bai, Xiaofei Zhou, and Shirui Pan. 2021. A self-supervised learning framework for sequential recommendation. In 2021 International Joint Conference on Neural Networks (IJCNN). 1–8.
[26]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). 197–206.
[27]
Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
[28]
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. 2022. Diffusion-lm improves controllable text generation. Advances in Neural Information Processing Systems 35 (2022), 4328–4343.
[29]
Zihao Li, Aixin Sun, and Chenliang Li. 2023. DiffuRec: A Diffusion Model for Sequential Recommendation. arXiv preprint arXiv:2304.00686 (2023).
[30]
Jianxun Lian, Iyad Batal, Zheng Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, and Xing Xie. 2021. Multi-interest-aware user modeling for large-scale sequential recommendations. arXiv preprint arXiv:2102.09211 (2021).
[31]
Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. 2023. Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples. In International Conference on Machine Learning. 20763–20786.
[32]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689–698.
[33]
Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, and Feng Tian. 2023. Diffusion Augmentation for Sequential Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1576–1586.
[34]
Siwei Liu, Xi Wang, Craig Macdonald, and Iadh Ounis. 2024. A Social-aware Gaussian Pre-trained model for effective cold-start recommendation. Information Processing & Management 61, 2 (2024), 103601.
[35]
James Lucas, George Tucker, Roger Grosse, and Mohammad Norouzi. 2019. Understanding posterior collapse in generative latent variable models. (2019).
[36]
Calvin Luo. 2022. Understanding diffusion models: A unified perspective. arXiv preprint arXiv:2208.11970 (2022).
[37]
Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, and Zhanhui Kang. 2024. Plug-in Diffusion Model for Sequential Recommendation. arXiv preprint arXiv:2401.02913 (2024).
[38]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. 1273–1282.
[39]
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020. Exploring data splitting strategies for the evaluation of recommendation models. In Proceedings of the 14th ACM Conference on Recommender Systems. 681–686.
[40]
Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen, Vu Tien Hoang, Dung D Le, and Kok-Seng Wong. 2024. Towards Efficient Communication Federated Recommendation System via Low-rank Training. arXiv preprint arXiv:2401.03748 (2024).
[41]
Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning. 8162–8171.
[42]
William Peebles and Saining Xie. 2023. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4195–4205.
[43]
Vasileios Perifanis, George Drosatos, Giorgos Stamatelatos, and Pavlos S Efraimidis. 2023. FedPOIRec: Privacy-preserving federated poi recommendation with social influence. Information Sciences 623 (2023), 767–790.
[44]
Vasileios Perifanis and Pavlos S Efraimidis. 2022. Federated neural collaborative filtering. Knowledge-Based Systems 242 (2022), 108441.
[45]
Yifang Qin, Wei Ju, Hongjun Wu, Xiao Luo, and Ming Zhang. 2024. Learning graph ODE for continuous-time sequential recommendation. IEEE Transactions on Knowledge and Data Engineering (2024).
[46]
Liang Qu, Wei Yuan, Ruiqi Zheng, Lizhen Cui, Yuhui Shi, and Hongzhi Yin. 2024. Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation. arXiv preprint arXiv:2401.17630 (2024).
[47]
Ahmed Rashed, Shereen Elsayed, and Lars Schmidt-Thieme. 2022. Context and attribute-aware sequential recommendation via cross-attention. In Proceedings of the 16th ACM Conference on Recommender Systems. 71–80.
[48]
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting. 2023. Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 22522–22531.
[49]
Hongjian Shi, Ruhui Ma, Dongmei Li, and Haibing Guan. 2023. Hierarchical Adaptive Collaborative Learning: A Distributed Learning Framework for Customized Cloud Services in 6G Mobile Systems. IEEE Network 37, 2 (2023), 44–53.
[50]
Hongjian Shi, Hao Wang, Ruhui Ma, Yang Hua, Tao Song, Honghao Gao, and Haibing Guan. 2023. Robust searching-based gradient collaborative management in intelligent transportation system. ACM Transactions on Multimedia Computing, Communications and Applications 20, 2 (2023), 1–23.
[51]
Hongjian Shi, Weichu Zheng, Zifei Liu, Ruhui Ma, and Haibing Guan. 2023. Automatic Pipeline Parallelism: A Parallel Inference Framework for Deep Learning Applications in 6G Mobile Communication Systems. IEEE Journal on Selected Areas in Communications (2023).
[52]
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning. 2256–2265.
[53]
Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining. 565–573.
[54]
Zuoli Tang, Lin Wang, Lixin Zou, Xiaolu Zhang, Jun Zhou, and Chenliang Li. 2023. Towards Multi-Interest Pre-training with Sparse Capsule Network. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 311–320.
[55]
Xicheng Wan, Yifeng Zheng, Qun Li, Anmin Fu, Mang Su, and Yansong Gao. 2022. Towards privacy-preserving and verifiable federated matrix factorization. Knowledge-Based Systems 250 (2022), 109193.
[56]
Chenyang Wang, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. Sequential recommendation with multiple contrast signals. ACM Transactions on Information Systems 41, 1 (2023), 1–27.
[57]
Chenyang Wang, Zhefan Wang, Yankai Liu, Yang Ge, Weizhi Ma, Min Zhang, Yiqun Liu, Junlan Feng, Chao Deng, and Shaoping Ma. 2022. Target Interest Distillation for Multi-Interest Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2007–2016.
[58]
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022. Causal representation learning for out-of-distribution recommendation. In Proceedings of the ACM Web Conference 2022. 3562–3571.
[59]
Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023. Diffusion Recommender Model. arXiv preprint arXiv:2304.04971 (2023).
[60]
Yu Wang, Zhiwei Liu, Liangwei Yang, and Philip S Yu. 2024. Conditional denoising diffusion for sequential recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. 156–169.
[61]
Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Counterfactual data-augmented sequential recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 347–356.
[62]
Shaowei Wei, Zhengwei Wu, Xin Li, Qintong Wu, Zhiqiang Zhang, Jun Zhou, Lihong Gu, and Jinjie Gu. 2024. Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation. In Proceedings of the ACM on Web Conference 2024. 3767–3776.
[63]
Wen Wen, Wencui Wang, Zhifeng Hao, and Ruichu Cai. 2023. Factorizing time-heterogeneous Markov transition for temporal recommendation. Neural Networks 159 (2023), 84–96.
[64]
Zihao Wu, Xin Wang, Hong Chen, Kaidong Li, Yi Han, Lifeng Sun, and Wenwu Zhu. 2023. Diff4Rec: Sequential Recommendation with Curriculum-scheduled Diffusion Augmentation. In Proceedings of the 31st ACM International Conference on Multimedia. 9329–9335.
[65]
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE). 1259–1273.
[66]
Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, and Qiang Yang. 2022. Practical and Secure Federated Recommendation with Personalized Mask. In International Workshop on Trustworthy Federated Learning. 33–45.
[67]
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2023. Diffusion models: A comprehensive survey of methods and applications. Comput. Surveys 56, 4 (2023), 1–39.
[68]
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019).
[69]
Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, and Xiangnan He. 2023. Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion. arXiv preprint arXiv:2310.20453 (2023).
[70]
Yuyu Yin, Youhuizi Li, Honghao Gao, Tingting Liang, and Qianqian Pan. 2022. FGC: GCN-Based Federated Learning Approach for Trust Industrial Service Recommendation. IEEE Transactions on Industrial Informatics 19, 3 (2022), 3240–3250.
[71]
Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2023. Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures. ACM Transactions on Information Systems (2023).
[72]
Yan Zeng, Yuyu Yin, Jilin Zhang, Meiting Xue, and Honghao Gao. 2023. FedPIA: Parameter Importance-Based Optimized Federated Learning to Efficiently Process Non-IID Data on Consumer Electronic Devices. IEEE Transactions on Consumer Electronics (2023).
[73]
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2023. A survey of controllable text generation using transformer-based pre-trained language models. Comput. Surveys 56, 3 (2023), 1–37.
[74]
Yupei Zhang, Yuxin Li, Yifei Wang, Shuangshuang Wei, Yunan Xu, and Xuequn Shang. 2024. Federated learning-outcome prediction with multi-layer privacy protection. Frontiers of Computer Science 18, 6 (2024), 186604.
[75]
Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, and Yongdong Zhang. 2023. Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation. arXiv preprint arXiv:2304.13643 (2023).
[76]
Yipeng Zhang, Xin Wang, Hong Chen, and Wenwu Zhu. 2023. Adaptive disentangled transformer for sequential recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3434–3445.
[77]
Yihu Zhang, Bo Yang, Haodong Liu, and Dongsheng Li. 2023. A time-aware self-attention based neural network model for sequential recommendation. Applied Soft Computing 133 (2023), 109894.
[78]
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu. 2023. Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs. arXiv preprint arXiv:2305.03935 (2023).
[79]
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM international conference on information & knowledge management. 1893–1902.
Index Terms
- Federated Recommender System Based on Diffusion Augmentation and Guided Denoising
Recommendations
Denoising Diffusion Recommender Model
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalRecommender systems often grapple with noisy implicit feedback. Most studies alleviate the noise issues from data cleaning perspective such as data resampling and reweighting, but they are constrained by heuristic assumptions. Another denoising avenue is ...
Diffusion Recommender Model
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalGenerative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, they suffer from intrinsic limitations such as the instability of ...
Diffusion Augmentation for Sequential Recommendation
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementSequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data ...
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Publication History
Online AM: 13 August 2024
Accepted: 08 August 2024
Revised: 15 July 2024
Received: 25 January 2024
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