Wenhao Yang
ABSTRACT
Cross-domain recommendation (CDR) aims to leverage the rich information from the source domain to enhance recommendation performance in the target domain. However, the data imbalance problem inherent across different domains compromises the effectiveness of CDR approaches, posing a significant challenge to CDR. Most current CDR methodologies focus on creating better user embeddings for the target domain, yet usually neglect the inconsistency in user activities due to data imbalance. As a result, the process of creating user embeddings tends to prioritize users with more frequent interactions and leave less active users underserved, leading these CDR methods to struggle in making accurate recommendations for those with fewer interactions. Such bias in creating embeddings reveals the fact that ''not all embeddings are created equal'' in CDR, which serves as the primary motivation of this study. Inspired by the recent development of contrastive learning, this paper proposes User-aware Contrastive Learning for Robust cross-domain recommendation (UCLR), enhancing the robustness of cross-domain recommendation. Specifically, our proposed method consists of two sub-modules: (i) pretrained global embedding, where the global user embeddings are pretrained across all the domains; (ii) contrastive dual-stream collaborative autoencoder, where more equal user embeddings are generated by optimizing contrastive loss with individualized temperatures. To further improve the performance of our method in each domain, we finetune the whole framework of UCLR based on Low-Rank Adaptation (LoRA). Theoretically, our method is equipped with a provable convergence guarantee during the contrastive learning stage. Furthermore, we also conduct comprehensive experiments on real-world datasets to validate the effectiveness of our proposed method.
Supplemental Material
- Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer. 2021. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 7319--7328.Google Scholar
Cross Ref
- Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, and Bin Wang. 2023. Towards Universal Cross-Domain Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 78--86.Google ScholarDigital Library
- Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu, and Bin Wang. 2022. DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 267--277.Google ScholarDigital Library
- Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. In Advances in Neural Information Processing Systems. 9912--9924.Google Scholar
- Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, and Kun Gai. 2023. PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3795--3804.Google ScholarDigital Library
- Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023 a. Bias and Debias in Recommender System: A Survey and Future Directions. ACM Transactions on Information Systems (2023).Google Scholar
- Junya Chen, Zhe Gan, Xuan Li, Qing Guo, Liqun Chen, Shuyang Gao, Tagyoung Chung, Yi Xu, Belinda Zeng, Wenlian Lu, et al. 2021. Simpler, faster, stronger: Breaking the log-k curse on contrastive learners with flatnce. arXiv preprint arXiv:2107.01152 (2021).Google Scholar
- Ling-Hao Chen, JiaWei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, and Tongliang Liu. 2023 b. HumanMAC: Masked Motion Completion for Human Motion Prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9544--9555.Google ScholarCross Ref
- Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning. 1597--1607.Google Scholar
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7--10.Google ScholarDigital Library
- Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. 2020. Debiased Contrastive Learning. In Advances in Neural Information Processing Systems. 8765--8775.Google Scholar
- Qiang Cui, Tao Wei, Yafeng Zhang, and Qing Zhang. 2020. HeroGRAPH: A heterogeneous graph framework for multi-target cross-domain recommendation. Proceedings of the 3rd Workshop on Online Recommender Systems and User Modeling co-located with the 14th ACM Conference on Recommender Systems (2020).Google Scholar
- Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. 2021. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9588--9597.Google ScholarCross Ref
- Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. In Proceedings of the 24th International Conference on World Wide Web. 278--288.Google ScholarDigital Library
- Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, and David Jacobs. 2021. Robust Contrastive Learning Using Negative Samples with Diminished Semantics. In Advances in Neural Information Processing Systems. 27356--27368.Google Scholar
- Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, et al. 2021. Self-supervised pretraining of visual features in the wild. arXiv preprint arXiv:2103.01988 (2021).Google Scholar
- Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma. 2021. Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. In Advances in Neural Information Processing Systems. 5000--5011.Google Scholar
- Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020b. Momentum Contrast for Unsupervised Visual Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9729--9738.Google ScholarCross Ref
- Ruining He and Julian McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proceedings of the 25th International Conference on World Wide Web. 507--517.Google ScholarDigital Library
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020a. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639--648.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173--182.Google ScholarDigital Library
- Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations.Google Scholar
- Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, and Jiashi Feng. 2022. Contrastive masked autoencoders are stronger vision learners. arXiv preprint arXiv:2207.13532 (2022).Google Scholar
- Kalervo J"arvelin and Jaana Kek"al"ainen. 2002. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transaction on Information Systems (2002), 422--446.Google Scholar
- Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. 2020. Hard Negative Mixing for Contrastive Learning. In Advances in Neural Information Processing Systems. 21798--21809.Google Scholar
- Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, and Taiji Suzuki. 2019. Cross-Domain Recommendation via Deep Domain Adaptation. In European Conference on Information Retrieval. 20--29.Google ScholarDigital Library
- SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1563--1572.Google ScholarDigital Library
- Diederik P. Kingma and Jimmy Lei Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations.Google Scholar
- Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1748--1757.Google ScholarDigital Library
- Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang, and Hari Sundaram. 2020. Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1081--1090.Google ScholarDigital Library
- Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski. 2018. Measuring the Intrinsic Dimension of Objective Landscapes. In International Conference on Learning Representations.Google Scholar
- Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, Xiaohu Qie, and Di Niu. 2023 a. One for All, All for One: Learning and transferring user embeddings for cross-domain recommendation. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 366--374.Google ScholarDigital Library
- Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, BeiBei Kong, and Di Niu. 2022. RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 571--581.Google ScholarDigital Library
- Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. 2021b. Prototypical Contrastive Learning of Unsupervised Representations. In International Conference on Learning Representations.Google Scholar
- Pan Li and Alexander Tuzhilin. 2020. DDTCDR: Deep dual transfer cross domain recommendation. In Proceedings of the 13rd ACM International Conference on Web Search and Data Mining. 331--339.Google ScholarDigital Library
- Pan Li and Alexander Tuzhilin. 2023. Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations. IEEE Transactions on Knowledge and Data Engineering (2023), 321--334.Google Scholar
- Siqing Li, Liuyi Yao, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Tonglei Guo, Bolin Ding, and Ji-Rong Wen. 2021a. Debiasing Learning based Cross-domain Recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3190--3199.Google ScholarDigital Library
- Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, and Ruiming Tang. 2023 b. HAMUR: Hyper Adapter for Multi-Domain Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1268--1277.Google ScholarDigital Library
- Jian Liu, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Xiaofang Zhou, and Hui Xiong. 2020b. Exploiting Aesthetic Preference in Deep Cross Networks for Cross-Domain Recommendation. In Proceedings of The Web Conference 2020. 2768--2774.Google ScholarDigital Library
- Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020a. Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 885--894.Google ScholarDigital Library
- Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-Domain Recommendation: An Embedding and Mapping Approach. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2464--2470.Google ScholarCross Ref
- Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).Google Scholar
- Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, and Tianbao Yang. 2023. Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. In Proceedings of the 40th International Conference on Machine Learning. 28389--28421.Google Scholar
- Dragomir R. Radev, Hong Qi, Harris Wu, and Weiguo Fan. 2002. Evaluating Web-based Question Answering Systems. In Proceedings of the Third International Conference on Language Resources and Evaluation.Google Scholar
- Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning. 8748--8763.Google Scholar
- Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Lim Ee Peng, and Yanjie Fu. 2022. Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective. In IEEE International Conference on Data Mining. 438--447.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452--461.Google ScholarDigital Library
- Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. 2021. Contrastive Learning with Hard Negative Samples. In International Conference on Learning Representations.Google Scholar
- Ashish Kumar Sahu and Pragya Dwivedi. 2020. Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems. Future Generation Computer Systems (2020), 320--333.Google Scholar
- Alex Tamkin, Mike Wu, and Noah Goodman. 2021. Viewmaker Networks: Learning Views for Unsupervised Representation Learning. In International Conference on Learning Representations. https://openreview.net/forum?id=enoVQWLsfyLGoogle Scholar
- Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What Makes for Good Views for Contrastive Learning?. In Advances in Neural Information Processing Systems. 6827--6839.Google Scholar
- Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic. 2022. Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? arXiv preprint arXiv:2201.05119 (2022).Google Scholar
- Cheng Wang, Mathias Niepert, and Hui Li. 2020. RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems. IEEE Transactions on Neural Networks and Learning Systems 8 (2020), 2731--2740.Google ScholarCross Ref
- Feng Wang and Huaping Liu. 2021. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2495--2504.Google ScholarCross Ref
- Lei Wang, Ee-Peng Lim, Zhiwei Liu, and Tianxiang Zhao. 2022. Explanation Guided Contrastive Learning for Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2017--2027.Google ScholarDigital Library
- Xiao Wang and Guo-Jun Qi. 2022. Contrastive learning with stronger augmentations. IEEE transactions on pattern analysis and machine intelligence (2022), 5549--5560.Google ScholarDigital Library
- Yaqing Wang, Chunyan Feng, Caili Guo, Yunfei Chu, and Jenq-Neng Hwang. 2019. Solving the Sparsity Problem in Recommendations via Cross-Domain Item Embedding Based on Co-Clustering. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 717--725.Google ScholarDigital Library
- Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, and Noah Goodman. 2021. Conditional Negative Sampling for Contrastive Learning of Visual Representations. In International Conference on Learning Representations.Google Scholar
- Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, and Tongliang Liu. 2022. Pluralistic Image Completion with Gaussian Mixture Models. In Advances in Neural Information Processing Systems, Vol. 35. 24087--24100.Google Scholar
- Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy. 2022. Delving into inter-image invariance for unsupervised visual representations. International Journal of Computer Vision (2022), 2994--3013.Google ScholarDigital Library
- Huan Yan, Chunfeng Yang, Donghan Yu, Yong Li, Depeng Jin, and Dah Ming Chiu. 2021. Multi-Site User Behavior Modeling and Its Application in Video Recommendation. IEEE Transactions on Knowledge and Data Engineering (2021), 180--193.Google ScholarDigital Library
- Longqi Yang, Yin Cui, Yuan Xuan, Chenyang Wang, Serge Belongie, and Deborah Estrin. 2018. Unbiased Offline Recommender Evaluation for Missing-Not-at-Random Implicit Feedback. In Proceedings of the 12th ACM Conference on Recommender Systems. 279--287.Google ScholarDigital Library
- Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, and Xiuqiang He. 2022. Contrastive Learning with Positive-Negative Frame Mask for Music Representation. In Proceedings of the ACM Web Conference 2022. 2906--2915.Google ScholarDigital Library
- Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, and Yao Li. 2017. Scalable Demand-Aware Recommendation. In Advance in Neural Information Processing Systems 30. 2409--2418.Google Scholar
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974--983.Google ScholarDigital Library
- Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 4227--4233.Google ScholarCross Ref
- Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, and Tianbao Yang. 2022. Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. In Proceedings of the 39th International Conference on Machine Learning. 25760--25782.Google Scholar
- Chaoning Zhang, Kang Zhang, Trung X Pham, Axi Niu, Zhinan Qiao, Chang D Yoo, and In So Kweon. 2022. Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14441--14450.Google ScholarCross Ref
- Yang Zhang, Yue Shen, Dong Wang, Jinjie Gu, and Guannan Zhang. 2023. Connecting Unseen Domains: Cross-Domain Invariant Learning in Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1894--1898.Google ScholarDigital Library
- Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun. 2020. CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 229--238.Google ScholarDigital Library
- Chuang Zhao, Hongke Zhao, Ming HE, Jian Zhang, and Jianping Fan. 2023. Cross-Domain Recommendation via User Interest Alignment. In Proceedings of the ACM Web Conference 2023. 887--896.Google ScholarDigital Library
- Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. DTCDR: A Framework for Dual-Target Cross-Domain Recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1533--1542.Google ScholarDigital Library
- Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. 3001--3008.Google ScholarCross Ref
- Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. 950--958.Google ScholarCross Ref
- Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, and Guanfeng Liu. 2023. A Unified Framework for Cross-Domain and Cross-System Recommendations. IEEE Transactions on Knowledge and Data Engineering (2023), 1171--1184.Google Scholar
- Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized Transfer of User Preferences for Cross-Domain Recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1507--1515. ioGoogle ScholarDigital Library
Index Terms
- Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning
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