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Disentangled Representations Learning for Multi-target Cross-domain Recommendation

Published: 23 March 2023 Publication History

Abstract

Data sparsity has been a long-standing issue for accurate and trustworthy recommendation systems (RS). To alleviate the problem, many researchers pay much attention to cross-domain recommendation (CDR), which aims at transferring rich knowledge from related source domains to enhance the recommendation performance of sparse target domain. To reach the knowledge transferring purpose, recent CDR works always focus on designing different pairwise directed or undirected information transferring strategies between source and target domains. However, such pairwise transferring idea is difficult to adapt to multi-target CDR scenarios directly, e.g., transferring knowledge between multiple domains and improving their performance simultaneously, as such strategies may lead the following issues: (1) When the number of domains increases, the number of transferring modules will grow exponentially, which causes heavy computation complexity. (2) A single pairwise transferring module could only capture the relevant information of two domains, but ignores the correlated information of other domains, which may limit the transferring effectiveness. (3) When a sparse domain serves as the source domain during the pairwise transferring, it would easily leads the negative transfer problem, and the untrustworthy information may hurt the target domain recommendation performance. In this article, we consider the key challenge of the multi-target CDR task: How to identify the most valuable trustworthy information over multiple domains and transfer such information efficiently to avoid the negative transfer problem? To fulfill the above challenge, we propose a novel end-to-end model termed as DR-MTCDR, standing for Disentangled Representations learning for Multi-Target CDR. DR-MTCDR aims at transferring the trustworthy domain-shared information across domains, which has the two major advantages in both efficiency and effectiveness: (1) For efficiency, DR-MTCDR utilizes a unified module on all domains to capture disentangled domain-shared information and domain-specific information, which could support all domain recommendation and be insensitive to the number of domains. (2) For effectiveness, based on the disentangled domain-shared and domain-specific information, DR-MTCDR has the capability to lead positive effect and make trustworthy recommendation for each domain. Empirical evaluations on datasets from both public datasets and real-world large-scale financial datasets have shown that the proposed framework outperforms other state-of-the-art baselines.

References

[1]
Deepak Agarwal, Bee-Chung Chen, and Bo Long. 2011. Localized factor models for multi-context recommendation. In International Conference on ACM Knowledge Discovery and Data Mining (KDD’11).
[2]
Yang Bao, Hui Fang, and Jie Zhang. 2014. TopicMF: Simultaneously exploiting ratings and reviews for recommendation. In AAAI Conference on Artificial Intelligence (AAAI’14).
[3]
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In International Conference on Machine Learning (ICML’18).
[4]
Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020. Bundle recommendation with graph convolutional networks. In International Conference on Research on Development in Information Retrieval (SIGIR’20).
[5]
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, and David Duvenaud. 2018. Isolating sources of disentanglement in variational autoencoders. In Annual Conference on Neural Information Processing Systems (NeurIPS’18).
[6]
Qiang Cui, Tao Wei, Yafeng Zhang, and Qing Zhang. 2020. HeroGRAPH: A heterogeneous graph framework for multi-target cross-domain recommendation. In RecSys Workshop.
[7]
Alexander Dallmann, Daniel Zoller, and Andreas Hotho. 2021. A case study on sampling strategies for evaluating neural sequential item recommendation models. In ACM Conference on Recommender systems (RecSys’21).
[8]
Aleksandr Farseev, Ivan Samborskii, Andrey Filchenkov, and Tat-Seng Chua. 2017. Cross-domain recommendation via clustering on multi-layer graphs. In International Conference on Research on Development in Information Retrieval (SIGIR’17).
[9]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (ICML’17).
[10]
Abel Gonzalez-Garcia, Joost Van De Weijer, and Yoshua Bengio. 2018. Image-to-image translation for cross-domain disentanglement. In Annual Conference on Neural Information Processing Systems (NeurIPS’18).
[11]
Hongyu Guo and Yongyi Mao. 2021. Intrusion-Free graph mixup. CoRR, abs/2110.09344 (2021). https://arxiv.org/abs/2110.09344
[12]
Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv preprint arXiv:2105.03300 (2021).
[13]
Lei Guo, Jinyu Zhang, Tong Chen, Xinhua Wang, and Hongzhi Yin. 2022. Reinforcement learning-enhanced shared-account cross-domain sequential recommendation. CoRR, abs/2206.08088 (2022).
[14]
Nur Al Hasan Haldar, Jianxin Li, Mark Reynolds, Timos Sellis, and Jeffrey Xu Yu. 2019. Location prediction in large-scale social networks: An in-depth benchmarking study. VLDB J. 28, 5 (2019), 623–648.
[15]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Annual Conference on Neural Information Processing Systems (NeurIPS’17).
[16]
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu. 2022. G-Mixup: Graph data augmentation for graph classification. International Conference on Machine Learning, (ICML’22, 17–23 July 2022, Baltimore, Maryland, (USA)), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (Eds.). Vol. 162, Proceedings of Machine Learning Research, PMLR, 8230–8248. https://proceedings.mlr.press/v162/han22c.html.
[17]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In International Conference on Research on Development in Information Retrieval (SIGIR’20).
[18]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In International World Wide Web Conferences (WWW’17).
[19]
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations (ICLR’17).
[20]
Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. CoNet: Collaborative cross networks for cross-domain recommendation. In ACM International Conference on Information and Knowledge Management (CIKM’18).
[21]
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019).
[22]
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun. 2020. GPT-GNN: Generative pre-training of graph neural networks. In International Conference on ACM Knowledge Discovery and Data Mining (KDD’20).
[23]
Ling Huang, Zhi-Lin Zhao, Chang-Dong Wang, Dong Huang, and Hong-Yang Chao. 2019. LSCD: Low-rank and sparse cross-domain recommendation. Neurocomputing 366 (2019), 86–96.
[24]
SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In ACM International Conference on Information and Knowledge Management (CIKM’19).
[25]
P. Diederik Kingma and Lei Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR’15).
[26]
Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[27]
Walid Krichene and Steffen Rendle. 2021. On sampled metrics for item recommendation (Extended Abstract). In Proceedings of the 30th International Joint Conference on Artificial Intelligence, (IJCAI’21, Virtual Event/Montreal, Canada, 19–27 August 2021), Zhi-Hua Zhou (Ed.). ijcai.org.
[28]
Dong Li, Ruoming Jin, Jing Gao, and Zhi Liu. 2020. On sampling top-k recommendation evaluation. In International Conference on ACM Knowledge Discovery and Data Mining (KDD’20).
[29]
Pan Li and Alexander Tuzhilin. 2020. DDTCDR: Deep dual transfer cross domain recommendation. In ACM International Conference on Web Search and Data Mining (WSDM’20).
[30]
Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020. Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In ACM International Conference on Information and Knowledge Management (CIKM’20).
[31]
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Why I like it: Multi-task learning for recommendation and explanation. In ACM Conference on Recommender systems (RecSys’18).
[32]
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. 2021. Learning to pre-train graph neural networks. In AAAI Conference on Artificial Intelligence (AAAI’21).
[33]
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H. Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Conference on ACM Knowledge Discovery and Data Mining (KDD’18).
[34]
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In International Joint Conference on Artificial Intelligence (IJCAI’17).
[35]
Maria Marrium and Arif Mahmood. 2022. Data augmentation for graph data: Recent advancements. CoRR, abs/2208.11973 (2022).
[36]
Michael F. Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun. 2016. Disentangling factors of variation in deep representation using adversarial training. In Annual Conference on Neural Information Processing Systems (NeurIPS’16).
[37]
Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: Understanding rating dimensions with review text. In ACM Conference on Recommender Systems (RecSys’13). 165–172.
[38]
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).
[39]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[40]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In International World Wide Web Conferences (WWW’01).
[41]
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng et al. 2021. One model to serve all: Star topology adaptive recommender for multi-domain CTR prediction. In ACM International Conference on Information and Knowledge Management (CIKM’21).
[42]
Ajit P. Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In ACM Knowledge Discovery and Data Mining (KDD’08).
[43]
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou. 2021. MoCL: Data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph. In International Conference on ACM Knowledge Discovery and Data Mining (KDD’21).
[44]
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. Representation learning with contrastive predictive coding. CoRR, abs/1807.03748 (2018). http://arxiv.org/abs/1807.03748
[45]
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep graph infomax. In International Conference on Learning Representations (ICLR’19).
[46]
Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, and Philip S. Yu. 2021. Pre-training graph neural network for cross domain recommendation. arXiv preprint arXiv:2111.08268 (2021).
[47]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In International Conference on Research on Development in Information Retrieval (SIGIR’19).
[48]
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 ACM International Conference on Web Search and Data Mining (WSDM’19).
[49]
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi. 2020. GraphCrop: Subgraph cropping for graph classification. CoRR, abs/2009.10564 (2020). https://arxiv.org/abs/2009.10564
[50]
Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Counterfactual data-augmented sequential recommendation. In International Conference on Research on Development in Information Retrieval (SIGIR’21).
[51]
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International Conference on Machine Learning (ICML’19).
[52]
Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2022. Contrastive cross-domain recommendation in matching. In 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
[53]
Li Yingzhen and Stephan Mandt. 2018. Disentangled sequential autoencoder. In International Conference on Machine Learning (ICML’18).
[54]
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In Annual Conference on Neural Information Processing Systems (NeurIPS’20).
[55]
Cheng Zhao, Chenliang Li, and Cong Fu. 2019. Cross-domain recommendation via Preference Propagation GraphNet. In ACM International Conference on Information and Knowledge Management (CIKM’19).
[56]
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 International Conference on Research on Development in Information Retrieval (SIGIR’20).
[57]
Lili Zhao, Sinno Jialin Pan, Evan Wei Xiang, Erheng Zhong, Zhongqi Lu, and Qiang Yang. 2013. Active transfer learning for cross-system recommendation. In AAAI Conference on Artificial Intelligence (AAAI’13).
[58]
Lili Zhao, Sinno Jialin Pan, and Qiang Yang. 2017. A unified framework of active transfer learning for cross-system recommendation. Artif. Intell. 245 (2017), 38–55.
[59]
Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019. DTCDR: A framework for dual-target cross-domain recommendation. In ACM International Conference on Information and Knowledge Management (CIKM’19).
[60]
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A graphical and attentional framework for dual-target cross-domain recommendation. In International Joint Conference on Artificial Intelligence (IJCAI’20).
[61]
Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: Challenges, progress, and prospects. arXiv preprint arXiv:2103.01696 (2021).
[62]
Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, and Guanfeng Liu. 2021. A unified framework for cross-domain and cross-system recommendations. IEEE Trans. Knowl. Data Eng. 35, 2 (2023), 1171–1184.
[63]
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, and Qing He. 2021. Transfer-meta framework for cross-domain recommendation to cold-start users. In ACM International Conference on Research on Development in Information Retrieval (SIGIR’21).
[64]
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 ACM International Conference on Web Search and Data Mining (WSDM’22).

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      cover image ACM Transactions on Information Systems
      ACM Transactions on Information Systems  Volume 41, Issue 4
      October 2023
      958 pages
      ISSN:1046-8188
      EISSN:1558-2868
      DOI:10.1145/3587261
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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 March 2023
      Online AM: 02 December 2022
      Accepted: 09 November 2022
      Revised: 16 September 2022
      Received: 30 May 2022
      Published in TOIS Volume 41, Issue 4

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      Author Tags

      1. Disentanglement representation learning
      2. cross domain recommendation
      3. trustworthy recommendation

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