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Personalized Item Recommendation for Second-hand Trading Platform

Authors:

Liqiang NieAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 3478 - 3486

https://doi.org/10.1145/3394171.3413640

Published: 12 October 2020 Publication History

Abstract

With rising awareness of environment protection and recycling, second-hand trading platforms have attracted increasing attention in recent years. The interaction data on second-hand trading platforms, consisting of sufficient interactions per user but rare interactions per item, is different from what they are on traditional platforms. Therefore, building successful recommendation systems in the second-hand trading platforms requires balancing modeling items? and users? preference, and mitigating the adverse effects of the sparsity, which makes recommendation especially challenging. Accordingly, we proposed a method to simultaneously learn representations of items and users from coarse-grained and fine-grained features, and a multi-task learning strategy is designed to address the issue of data sparsity. Experiments conducted on a real-world second-hand trading platform dataset demonstrate the effectiveness of our proposed model.

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MP4 File (3394171.3413640.mp4)

With rising awareness of environment protection and recycling, second-hand trading platforms have attracted increasing attention in recent years. The interaction data on second-hand trading platforms, consisting of sufficient interactions per user but rare interactions per item, is different from what they are on traditional platforms. Therefore, building successful recommendation systems in the second-hand trading platforms requires balancing modeling items? and users? preference, and mitigating the adverse effects of the sparsity, which makes recommendation especially challenging. Accordingly, we proposed a method to simultaneously learn representations of items and users from coarse-grained and fine-grained features, and a multi-task learning strategy is designed to address the issue of data sparsity. Experiments conducted on a real-world second-hand trading platform dataset demonstrate the effectiveness of our proposed model.

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References

[1]

G Adomavicius and A Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, Vol. 17, 6 (2005), 734--749.

Digital Library

[2]

Daniel Billsus and Michael J. Pazzani. 1998. Learning Collaborative Information Filters. In Proceedings of International Conference on Machine Learning, ICML. 46--54.

[3]

Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose Catherine Kanjirathinkal, and Mohan S. Kankanhalli. 2019. MMALFM: Explainable Recommendation by Leveraging Reviews and Images. ACM Transactions on Information Systems, Vol. 37, 2 (2019), 16:1--16:28.

Digital Library

[4]

Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan S Kankanhalli. 2018a. A3NCF: An Adaptive Aspect Attention Model for Rating Prediction. In Proceedings of International Joint Conference on Artificial Intelligence, IJCAI.

[5]

Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan S. Kankanhalli. 2018b. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. In Proceedings of World Wide Web Conference, WWW. ACM, 639--648.

[6]

Zhiyong Cheng, Jialie Shen, and Steven C. H. Hoi. 2016. On Effective Personalized Music Retrieval by Exploring Online User Behaviors. In Proceedings of International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 125--134.

[7]

Tian Gan, Junnan Li, Yongkang Wong, and Mohan S. Kankanhalli. 2019 a. A Multi-sensor Framework for Personal Presentation Analytics. Transaction on Multimedia Computing, Communications, and Applications, Vol. 15, 2 (2019), 30:1--30:21.

[8]

Tian Gan, Shaokun Wang, Meng Liu, Xuemeng Song, Yiyang Yao, and Liqiang Nie. 2019 b. Seeking Micro-Influencers for Brand Promotion. In Proceedings of the ACM International Conference on Multimedia. 1933--1941.

Digital Library

[9]

Tian Gan, Yongkang Wong, Bappaditya Mandal, Vijay Chandrasekhar, and Mohan S. Kankanhalli. 2015. Multi-sensor Self-Quantification of Presentations. In ACMMM. 601--610.

[10]

Tian Gan, Yongkang Wong, Daqing Zhang, and Mohan S. Kankanhalli. 2013. Temporal encoded F-formation system for social interaction detection. In Proceedings of ACM International Conference on Multimedia. 937--946.

[11]

C. Gao, X. He, D. Gan, X. Chen, F. Feng, Y. Li, T. Chua, L. Yao, Y. Song, and D. Jin. 2019. Learning to Recommend with Multiple Cascading Behaviors. IEEE Transactions on Knowledge and Data Engineering (2019), 1--1.

[12]

Yuyun Gong and Qi Zhang. 2016. Hashtag Recommendation Using Attention-Based Convolutional Neural Network. In Proceedings of International Joint Conference on Artificial Intelligence, IJCAI, Subbarao Kambhampati (Ed.). IJCAI/AAAI Press, 2782--2788.

[13]

Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020. Hierarchical User Profiling for E-commerce Recommender Systems. In Proceedings of ACM International Conference on Web Search and Data Mining, WSDM. 223--231.

Digital Library

[14]

Yangyang Guo, Zhiyong Cheng, Jiazheng Jing, Yanpeng Lin, Liqiang Nie, and Meng Wang. 2020. Enhancing Factorization Machines with Generalized Metric Learning. arXiv preprint arXiv:2006.11600 (2020).

[15]

Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Xin-Shun Xu, and Mohan S. Kankanhalli. 2018. Multi-modal Preference Modeling for Product Search. In Proceedings of ACM International Conference on Multimedia. 1865--1873.

[16]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016b. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. IEEE Computer Society, 770--778.

[17]

Ruining He, Chunbin Lin, Jianguo Wang, and Julian J. McAuley. 2016a. Sherlock: Sparse Hierarchical Embeddings for Visually-Aware One-Class Collaborative Filtering. In Proceedings of International Joint Conference on Artificial Intelligence, IJCAI. 3740--3746.

[18]

Ruining He and Julian McAuley. 2016. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of AAAI Conference on Artificial Intelligence. 144--150.

[19]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 639--648.

Digital Library

[20]

Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, and Mohan Kankanhalli. 2019. User Diverse Preference Modeling by Multimodal Attentive Metric Learning. In Proceedings of ACM International Conference on Multimedia. 1526--1534.

Digital Library

[21]

Qiang Liu, Shu Wu, and Liang Wang. 2017. DeepStyle: Learning User Preferences for Visual Recommendation. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 841--844.

Digital Library

[22]

Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In International Conference on Learning Representations, ICLR.

[23]

Andreas Loukas. 2020. What graph neural networks cannot learn: depth vs width. In International Conference on Learning Representations, ICLR.

[24]

Michael J. Pazzani. 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, Vol. 13, 5--6 (1999), 393--408.

Digital Library

[25]

Yogesh Singh Rawat and Mohan S. Kankanhalli. 2016. ConTagNet: Exploiting User Context for Image Tag Recommendation. In Proceedings of ACM International Conference on Multimedia. ACM, 1102--1106.

[26]

R. Salakhutdinov and A. Mnih. 2008. Probabilistic matrix factorization. Advances in Neural Information Processing Systems (2008), 1257--1264.

[27]

Andreas Veit, Maximilian Nickel, Serge J. Belongie, and Laurens van der Maaten. 2018. Separating Self-Expression and Visual Content in Hashtag Supervision. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. IEEE Computer Society, 5919--5927.

[28]

Mengting Wan and Julian J. McAuley. 2018. Item recommendation on monotonic behavior chains. In Proceedings of ACM Conference on Recommender Systems, RecSys. 86--94.

[29]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 165--174.

Digital Library

[30]

Yinwei Wei, Zhiyong Cheng, Xuzheng Yu, Zhou Zhao, Lei Zhu, and Liqiang Nie. 2019 a. Personalized Hashtag Recommendation for Micro-videos. In Proceedings of ACM International Conference on Multimedia. ACM, 1446--1454.

Digital Library

[31]

Yinwei Wei, Xiang Wang, Weili Guan, Liqiang Nie, Zhouchen Lin, and Baoquan Chen. 2019 b. Neural multimodal cooperative learning toward micro-video understanding. IEEE Transactions on Image Processing, Vol. 29 (2019), 1--14.

[32]

Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019 c. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of ACM International Conference on Multimedia. 1437--1445.

Digital Library

[33]

Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. In Proceedings of International Conference on Machine Learning, ICML, Vol. 97. 6861--6871.

[34]

Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018a. ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation. In Proceedings of AAAI Conference on Artificial Intelligence, Innovative Applications of Artificial Intelligence, and AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press, 4564--4571.

[35]

Meizi Zhou, Zhuoye Ding, Jiliang Tang, and Dawei Yin. 2018b. Micro Behaviors: A New Perspective in E-commerce Recommender Systems. In Proceedings of ACM International Conference on Web Search and Data Mining, WSDM. 727--735.

Digital Library

Cited By

Ji WFei HWei YZheng ZLi JChen LLiao LZhuang YZimmermann RJi WFei HZheng ZFei HWei YZheng Z(2024)The 2nd International Workshop on Deep Multi-modal Generation and RetrievalProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690093(1-6)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690093
Ren DU LLiu WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Dual-view Contrastive Learning for Auction RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614854(2146-2155)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614854
Ji WWei YZheng ZFei HChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Deep Multimodal Learning for Information RetrievalProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3610949(9739-9741)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3610949
Show More Cited By

Index Terms

Personalized Item Recommendation for Second-hand Trading Platform
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Hierarchical data models
  2. Information retrieval

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Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 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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New York, NY, United States

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Published: 12 October 2020

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National Key Research and Development Project of New Generation Artificial Intelligence
Innovation Teams in Colleges and Universities in Jinan
Shandong Provincial Natural Science Foundation
Shandong Provincial Key Research and Development Program
National Natural Science Foundation of China

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Ji WFei HWei YZheng ZLi JChen LLiao LZhuang YZimmermann RJi WFei HZheng ZFei HWei YZheng Z(2024)The 2nd International Workshop on Deep Multi-modal Generation and RetrievalProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690093(1-6)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690093
Ren DU LLiu WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Dual-view Contrastive Learning for Auction RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614854(2146-2155)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614854
Ji WWei YZheng ZFei HChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Deep Multimodal Learning for Information RetrievalProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3610949(9739-9741)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3610949
Li ZLi JWang TGong XWei YLuo P(2022)OCPHN: Outfit Compatibility Prediction with Hypergraph NetworksMathematics10.3390/math1020391310:20(3913)Online publication date: 21-Oct-2022
https://doi.org/10.3390/math10203913
Liu HWei YYin JNie L(2022)HS-GCN: Hamming Spatial Graph Convolutional Networks for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3158317(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3158317
An SChe GGuo JZhu HYe JZhou FZhu ZWei DLiu AZhang WShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)ARShoeProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3481537(1111-1119)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3481537

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