research-article

Open access

CFCR: A Convolution and Fusion Model for Cross-platform Recommendation

Authors:

Wenwu ZhuAuthors Info & Claims

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

Article No.: 65, Pages 1 - 6

https://doi.org/10.1145/3469877.3495639

Published: 10 January 2022 Publication History

All formats PDF

Abstract

With the emergence of various online platforms, associating different platforms is playing an increasingly important role in many applications. Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing methods do not fully exploit high-order nonlinear connectivity information in cross-domain recommendation scenario and suffer from domain-incompatibility problem. In this paper, we propose an end-to-end convolution and fusion model for cross-platform recommendation (CFCR). The proposed CFCR model utilizes Graph Convolution Networks (GCN) to extract user and item features on graphs from different platforms, and fuses cross-platform information by Multimodal AutoEncoder (MAE) with common latent user features. Therefore, the high-order connectivity information is preserved to the most extent and domain-invariant user representations are automatically obtained. The domain-incompatible information is spontaneously discarded to avoid messing up the cross-platform association. Extensive experiments for the proposed CFCR model on real-world dataset demonstrate its advantages over existing cross-platform recommendation methods in terms of various evaluation metrics.

References

[1]

Leihui Chen, Jianbing Zheng, Ming Gao, Aoying Zhou, Wei Zeng, and Hui Chen. 2017. TLRec: transfer learning for cross-domain recommendation. In 2017 IEEE International Conference on Big Knowledge (ICBK). IEEE, 167–172.

[2]

Terence Chen, Mohamed Ali Kaafar, Arik Friedman, and Roksana Boreli. 2012. Is more always merrier?: a deep dive into online social footprints. In Proceedings of the 2012 ACM workshop on Workshop on online social networks. ACM, 67–72.

Digital Library

[3]

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.

Digital Library

[4]

Matthew Hoffman, Francis R Bach, and David M Blei. 2010. Online learning for latent dirichlet allocation. In advances in neural information processing systems. 856–864.

[5]

Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. 2016. Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds. In AAAI. 13–19.

[6]

How Jing, An-Chun Liang, Shou-De Lin, and Yu Tsao. 2014. A transfer probabilistic collective factorization model to handle sparse data in collaborative filtering. In Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 250–259.

Digital Library

[7]

Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 426–434.

Digital Library

[8]

Zhiwei Liu, Lei Zheng, Jiawei Zhang, Jiayu Han, and S Yu Philip. 2019. JSCN: Joint spectral convolutional network for cross domain recommendation. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 850–859.

[9]

Zhongqi Lu, Erheng Zhong, Lili Zhao, Evan Wei Xiang, Weike Pan, and Qiang Yang. 2013. Selective transfer learning for cross domain recommendation. In Proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, 641–649.

[10]

Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-Domain Recommendation: An Embedding and Mapping Approach. In Twenty-Sixth International Joint Conference on Artificial Intelligence. 2464–2470.

[11]

Weiqing Min, Bing Kun Bao, Changsheng Xu, and M. Shamim Hossain. 2015. Cross-Platform Multi-Modal Topic Modeling for Personalized Inter-Platform Recommendation. IEEE Transactions on Multimedia 17, 10 (2015), 1787–1801.

Digital Library

[12]

Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011. Multimodal deep learning. In Proceedings of the 28th international conference on machine learning (ICML-11). 689–696.

Digital Library

[13]

Shengsheng Qian, Tianzhu Zhang, Richang Hong, and Changsheng Xu. 2015. Cross-domain collaborative learning in social multimedia. In Proceedings of the 23rd ACM international conference on Multimedia. ACM, 99–108.

Digital Library

[14]

Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, and Jiawei Han. 2018. Easing embedding learning by comprehensive transcription of heterogeneous information networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2190–2199.

Digital Library

[15]

Xin Wang, Hong Chen, and Wenwu Zhu. 2021. Multimodal Disentangled Representation for Recommendation. In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[16]

Xin Wang, Wei Lu, Martin Ester, Can Wang, and Chun Chen. 2016. Social recommendation with strong and weak ties. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 5–14.

Digital Library

[17]

Xinghua Wang, Zhaohui Peng, Senzhang Wang, S Yu Philip, Wenjing Fu, and Xiaoguang Hong. 2018. Cross-domain recommendation for cold-start users via neighborhood based feature mapping. In International conference on database systems for advanced applications. Springer, 158–165.

Digital Library

[18]

Xin Wang, Wenwu Zhu, and Chenghao Liu. 2019. Social recommendation with optimal limited attention. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1518–1527.

Digital Library

[19]

Ming Yan, Jitao Sang, and Changsheng Xu. 2014. Mining cross-network association for youtube video promotion. In Proceedings of the 22nd ACM international conference on Multimedia. ACM, 557–566.

Digital Library

[20]

Ming Yan, Jitao Sang, and Changsheng Xu. 2015. Unified youtube video recommendation via cross-network collaboration. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, 19–26.

Digital Library

[21]

Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, and Jingdong Wang. 2019. Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation. In IJCAI.

[22]

Wenhui Yu and Zheng Qin. 2020. Graph convolutional network for recommendation with low-pass collaborative filters. In International Conference on Machine Learning. PMLR, 10936–10945.

[23]

Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, and Philip S Yu. 2018. Spectral collaborative filtering. In Proceedings of the 12th ACM conference on recommender systems. 311–319.

Digital Library

[24]

Wenwu Zhu, Xin Wang, and Wen Gao. 2020. Multimedia intelligence: When multimedia meets artificial intelligence. IEEE Transactions on Multimedia 22, 7 (2020), 1823–1835.

Cited By

Gan MXu GMa Y(2023)A multi-behavior recommendation method exploring the preference differences among various behaviorsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120316228:COnline publication date: 15-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120316

Index Terms

CFCR: A Convolution and Fusion Model for Cross-platform Recommendation

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

Recommendations

Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts
Special issue: Search, Mining and their Applications on Mobile Devices

Over the last decade, the renaissance of Web technologies has transformed the online world into an application (App) driven society. While the abundant Apps have provided great convenience, their sheer number also leads to severe information overload, ...
Pro Smartphone Cross-Platform Development: iPhone, Blackberry, Windows Mobile and Android Development and Distribution
Multicommunity Graph Convolution Networks with Decision Fusion for Personalized Recommendation
Advances in Knowledge Discovery and Data Mining
Abstract
Most graph-based recommendation approaches actually have made an implicit assumption about that representations of all users and all items can be learned in a single latent space. However, this assumption may be too strong to well describe a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

December 2021

508 pages

ISBN:9781450386074

DOI:10.1145/3469877

Copyright © 2021 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China
Tsinghua GuoQiang Research Center

Conference

MMAsia '21

Sponsor:

SIGMM

MMAsia '21: ACM Multimedia Asia

December 1 - 3, 2021

Gold Coast, Australia

Acceptance Rates

Overall Acceptance Rate 59 of 204 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
235
Total Downloads

Downloads (Last 12 months)105
Downloads (Last 6 weeks)26

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gan MXu GMa Y(2023)A multi-behavior recommendation method exploring the preference differences among various behaviorsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120316228:COnline publication date: 15-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120316

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten