research-article

Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network

Authors:

Zhiwen YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 4

Article No.: 55, Pages 1 - 21

https://doi.org/10.1145/3442201

Published: 18 April 2021 Publication History

Abstract

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

References

[1]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 8 (2013), 1798--1828.

Digital Library

[2]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations. 1--14.

[3]

Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, and Paolo Cremonesi. 2015. Cross-domain recommender systems. In Recommender Systems Handbook. Springer, 919--959.

Digital Library

[4]

Rich Caruana. 1997. Multitask learning. Machine Learning 28, 1 (1997), 41--75.

Digital Library

[5]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for Youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191--198.

Digital Library

[6]

Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering 31, 5 (2018), 833--852.

[7]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the Advances in Neural Information Processing Systems. 3844--3852.

[8]

Jean-Antoine Désidéri. 2012. Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique 350, 5--6 (2012), 313--318.

[9]

Mihajlo Grbovic and Haibin Cheng. 2018. Real-time personalization using embeddings for search ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 311--320.

Digital Library

[10]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.

Digital Library

[11]

Bin Guo, Yi Ouyang, Tong Guo, Longbing Cao, and Zhiwen Yu. 2019. Enhancing mobile app user understanding and marketing with heterogeneous crowdsourced data: A review. IEEE Access 7 (2019), 68557--68571.

[12]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems. 1024--1034.

[13]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. IEEE Data Engineering Bulletin 40, 3 (2017), 52--74.

[14]

Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 667--676.

Digital Library

[15]

Martin Jaggi. 2013. Revisiting Frank-Wolfe: Projection-free sparse convex optimization. In Proceedings of the 30th International Conference on Machine Learning. 427--435.

[16]

Alexandros Karatzoglou, Linas Baltrunas, Karen Church, and Matthias Böhmer. 2012. Climbing the app wall: Enabling mobile app discovery through context-aware recommendations. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 2527--2530.

Digital Library

[17]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of 3rd International Conference on Learning Representations. 1--14.

[18]

Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations. 1--14.

[19]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37.

Digital Library

[20]

H. W. Kuhn and A. W. Tucker. 1951. Nonlinear programming. In Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probability. 481--492.

[21]

Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, and Tat-Seng Chua. 2013. Addressing cold-start in app recommendation: Latent user models constructed from twitter followers. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 283--292.

Digital Library

[22]

Bin Liu, Deguang Kong, Lei Cen, Neil Zhenqiang Gong, Hongxia Jin, and Hui Xiong. 2015. Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining. ACM, 315--324.

Digital Library

[23]

Yao Ma, Suhang Wang, Chara C. Aggarwal, Dawei Yin, and Jiliang Tang. 2019. Multi-dimensional graph convolutional networks. In Proceedings of the 2019 SIAM International Conference on Data Mining. SIAM, 657--665.

[24]

Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In Proceedings of the International Joint Conference on Artificial Intelligence. 2464--2470.

[25]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of the 1st International Conference on Learning Representations. 1--11.

[26]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the Advances in Neural Information Processing Systems. 3111--3119.

Digital Library

[27]

Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, Anxiang Zeng, and Luo Si. 2018. Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 596--605.

Digital Library

[28]

Yi Ouyang, Bin Guo, Tong Guo, Longbing Cao, and Zhiwen Yu. 2018. Modeling and forecasting the popularity evolution of mobile apps: A multivariate Hawkes process approach. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 182.

Digital Library

[29]

Yi Ouyang, Bin Guo, Xinjiang Lu, Qi Han, Tong Guo, and Zhiwen Yu. 2019. Competitivebike: Competitive analysis and popularity prediction of bike-sharing apps using multi-source data. IEEE Transactions on Mobile Computing 18, 8 (2019), 1760--1773.

[30]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701--710.

Digital Library

[31]

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. AUAI Press, 452--461.

Digital Library

[32]

Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017).

[33]

Ozan Sener and Vladlen Koltun. 2018. Multi-task learning as multi-objective optimization. In Proceedings of the Advances in Neural Information Processing Systems. 527--538.

[34]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077.

Digital Library

[35]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations. 1--12.

[36]

Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale commodity embedding for e-commerce recommendation in Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 839--848.

Digital Library

[37]

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 12th ACM International Conference on Web Search and Data Mining. ACM, 717--725.

Digital Library

[38]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4--24.

[39]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations. 1--17.

[40]

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.

Digital Library

[41]

Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019. Graph transformer networks. In Proceedings of the Advances in Neural Information Processing Systems. 11960--11970.

[42]

R. Zafarani and H. Liu. 2009. Social Computing Data Repository at ASU. Retrieved from http://socialcomputing.asu.edu.

[43]

Kui Zhao, Yuechuan Li, Zhaoqian Shuai, and Cheng Yang. 2018. Learning and transferring ids representation in e-commerce. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1031--1039.

Digital Library

[44]

Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434 (2018).

[45]

Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, and Jia Wu. 2018. A deep framework for cross-domain and cross-system recommendations. In Proceedings of the International Joint Conference on Artificial Intelligence.

[46]

Hengshu Zhu, Hui Xiong, Yong Ge, and Enhong Chen. 2014. Mobile app recommendations with security and privacy awareness. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 951--960.

Digital Library

[47]

Konglin Zhu, Lin Zhang, and Achille Pattavina. 2017. Learning geographical and mobility factors for mobile application recommendation. IEEE Intelligent Systems 32, 3 (2017), 36--44.

Digital Library

[48]

Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang, Xiang Ao, Xing Xie, and Qing He. 2017. Sequential transfer learning: Cross-domain novelty seeking trait mining for recommendation. In Proceedings of the 26th International Conference on World Wide Web Companion. 881--882.

Digital Library

Cited By

Zhang WZhang JZhang Y(2024)A Comment Aspect-Level User Preference Transfer Model for Cross-Domain RecommendationsInformation Resources Management Journal10.4018/IRMJ.34536037:1(1-25)Online publication date: 17-Sep-2024
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Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Han JTang YTao QXia YZhang L(2024)Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/365397618:6(1-24)Online publication date: 26-Mar-2024
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Index Terms

Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 4

August 2021

486 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3458847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 ACM.

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Publication History

Published: 18 April 2021

Accepted: 01 December 2020

Revised: 01 July 2020

Received: 01 March 2020

Published in TKDD Volume 15, Issue 4

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National Natural Science Foundation of China
National Key R&D Program of China
National Science Fund for Distinguished Young Scholars

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Cited By

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https://dl.acm.org/doi/10.4018/IRMJ.345360
Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Han JTang YTao QXia YZhang L(2024)Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/365397618:6(1-24)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3653976
Tang XQiao YLyu FLiu DHe X(2024)Touch the Core: Exploring Task Dependence Among Hybrid Targets for RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688101(329-339)Online publication date: 8-Oct-2024
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Liu SCao BLiu JKang GShi MLi XFletcher K(2024)An Approach for Privacy-Aware Mobile App Package RecommendationIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34430285:12(6240-6252)Online publication date: Dec-2024
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Lv Q(2024)E-Commerce Big Data Analysis and User Behavior Prediction Algorithm Based on Deep Learning2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)10.1109/AIARS63200.2024.00046(219-224)Online publication date: 29-Jul-2024
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Zeng FLi YXiao JYang D(2024)DDHCNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121564237:PBOnline publication date: 1-Feb-2024
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Wan SYang SLiu YDing JQiu DWei C(2023)An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation NetworkApplied Sciences10.3390/app1307438913:7(4389)Online publication date: 30-Mar-2023
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Hsu CChen CHuang S(2023)Adaptive Adversarial Contrastive Learning for Cross-Domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/363025918:3(1-34)Online publication date: 9-Dec-2023
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