research-article

Behavior Matching between Different Domains based on Canonical Correlation Analysis

Authors:

Daichi Amagata,

Takuya Maekawa,

Mori KurokawaAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 361 - 366

https://doi.org/10.1145/3308560.3316595

Published: 13 May 2019 Publication History

Abstract

With the recent proliferation of e-commerce services, online shopping has become more and more popular among customers. Because it is necessary to recommend proper items to customers, to improve the accuracy of recommendation, high-performance recommender systems are required. However, current recommender systems are mainly based on information of their own domain, resulting in low accurate recommendation for customers with limited purchasing histories. The accuracy may suffer due to a lack of information. In order to use information from other domains, it is necessary to associate behaviors in different domains of the behaviorally related users. This paper presents a preliminary analysis of matching behaviors of the behaviorally related users in different domains. The result shows that we got a better prediction rate than linear regression.

References

[1]

2013. Mecab. http://taku910.github.io/mecab/.

[2]

Galen Andrew, Raman Arora, Jeff Bilmes, and Karen Livescu. 2013. Deep canonical correlation analysis. In Proc. 2013 International Conference on Machine Learning. 1247–1255.

Digital Library

[3]

Francis R Bach and Michael I Jordan. Technical report, University of California, Berkeley, 2005. A probabilistic interpretation of canonical correlation analysis. (Technical report, University of California, Berkeley, 2005).

[4]

Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Vaughan. 2010. A theory of learning from different domains. Machine Learning 79, 151–175.

Digital Library

[5]

Patricia Cohen, Stephen G West, and Leona S Aiken. 2014. Applied multiple regression/correlation analysis for the behavioral sciences.

[6]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proc. 10th ACM Conference on Recommender Systems. 191–198.

Digital Library

[7]

Hal Daumé III, Abhishek Kumar, and Avishek Saha. 2010. Frustratingly easy semi-supervised domain adaptation. In Proc. the 2010 Workshop on Domain Adaptation for Natural Language Processing. 53–59.

Digital Library

[8]

Daniel Garcia-Romero and Alan McCree. 2014. Supervised domain adaptation for i-vector based speaker recognition. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4047–4051.

[9]

David R Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004. Canonical correlation analysis: An overview with application to learning methods. Neural computation 16, 12 (2004), 2639–2664.

Digital Library

[10]

Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, and C. Lee Giles. 2015. A Neural Probabilistic Model for Context Based Citation Recommendation. In Proc. 29th AAAI Conference on Artificial Intelligence. 2404–2410.

Digital Library

[11]

Thomas R Knapp. 1978. Canonical correlation analysis: A general parametric significance-testing system.Psychological Bulletin 85, 2 (1978), 410.

[12]

Pei Ling Lai and Colin Fyfe. 2000. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems 10, 05 (2000), 365–377.

[13]

Jey Han Lau and Timothy Baldwin. 2016. An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368(2016).

[14]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proc. 2014 International Conference on Machine Learning. 1188–1196.

Digital Library

[15]

Dongsheng Li, Chao Chen, Qin Lv, Hansu Gu, Tun Lu, Li Shang, Ning Gu, and Stephen M. Chu. 2018. AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering. In Proc. 2018 World Wide Web Conference. 741–751.

Digital Library

[16]

Gengxin Miao, Junichi Tatemura, Wang-Pin Hsiung, Arsany Sawires, and Louise E Moser. 2009. Extracting data records from the web using tag path clustering. In Proc. the 18th International Conference on World Wide Web. 981–990.

Digital Library

[17]

Shakiba Rahimiaghdam, Pinar Karagoz, and Alev Mutlu. 2016. Personalized Time-aware Outdoor Activity Recommendation System. In Proc. 31st Annual ACM Symposium on Applied Computing. 1121–1126.

Digital Library

[18]

Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada. 2017. Asymmetric Tri-training for Unsupervised Domain Adaptation. In Proc. 2017 International Conference on Machine Learning.

Digital Library

[19]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based Collaborative Filtering Recommendation Algorithms. In Proc. 10th International Conference on World Wide Web. 285–295.

Digital Library

[20]

George AF Seber and Alan J Lee. 2012. Linear regression analysis. Vol. 329.

Cited By

Murao JYonekawa KKurokawa MAmagata DMaekawa THara T(2022)Concept Drift Detection with Denoising Autoencoder in Incomplete DataMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_35(541-552)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_35

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Behavior Matching between Different Domains based on Canonical Correlation Analysis

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Cited By

Murao JYonekawa KKurokawa MAmagata DMaekawa THara T(2022)Concept Drift Detection with Denoising Autoencoder in Incomplete DataMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_35(541-552)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_35

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