research-article

Overview of Content-Based Click-Through Rate Prediction Challenge for Video Recommendation

Authors:

Yunsheng Jiang,

Xiaohui XieAuthors Info & Claims

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Pages 2593 - 2596

https://doi.org/10.1145/3343031.3356085

Published: 15 October 2019 Publication History

Abstract

Content cold-start is a core problem in recommendation field, by which service providers can mine the potential profit from content that has not yet been discovered by most users, and provide more accurate personalized service to their users. In video recommendation, video and audio features should cover enough semantic information in the purpose of recommendation, thus should take an non-negligible role for content cold-start. This paper summarizes the Content Based Video Relevance Prediction Challenge held by Hulu, a top online streaming video platform in US, in ACM Multimedia conference 2019. The challenge is a content-based CTR prediction task for video recommendation, where millions of user interaction data and thousands of video features are released for research purpose on related topics.

References

[1]

[n.d.]. AudioSet data description. https://research.google.com/audioset/download.html.

[2]

Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675 (2016).

[3]

Yash Bhalgat. 2018. FusedLSTM: Fusing frame-level and video-level features for Content-based Video Relevance Prediction. arXiv preprint arXiv:1810.00136 (2018).

[4]

Junxuan Chen, Baigui Sun, Hao Li, Hongtao Lu, and Xian-Sheng Hua. 2016. Deep ctr prediction in display advertising. In Proceedings of the 24th ACM international conference on Multimedia. ACM, 811--820.

Digital Library

[5]

Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017b. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 335--344.

Digital Library

[6]

Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, and Zheng Qin. 2017a. Personalized key frame recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 315--324.

Digital Library

[7]

Xusong Chen, Rui Zhao, Shengjie Ma, Dong Liu, and Zheng-Jun Zha. 2018. Content-Based Video Relevance Prediction with Second-Order Relevance and Attention Modeling. In 2018 ACM Multimedia Conference on Multimedia Conference. ACM.

[8]

Jianfeng Dong, Xirong Li, Chaoxi Xu, Gang Yang, and Xun Wang. 2018. Feature Re-Learning with Data Augmentation for Content-based Video Recommendation. In ACM Multimedia. 2058--2062.

[9]

Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, et almbox. 2018. Image matters: Visually modeling user behaviors using advanced model server. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2087--2095.

Digital Library

[10]

Xue Geng, Hanwang Zhang, Jingwen Bian, and Tat-Seng Chua. 2015. Learning image and user features for recommendation in social networks. In Proceedings of the IEEE International Conference on Computer Vision. 4274--4282.

Digital Library

[11]

David J Hand. 2009. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine learning, Vol. 77, 1 (2009), 103--123.

[12]

Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. 2014. Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 1725--1732.

Digital Library

[13]

Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et almbox. 2017. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017).

[14]

Yaman Kumar, Agniv Sharma, Abhigyan Khaund, Akash Kumar, Ponnurangam Kumaraguru, Rajiv Ratn Shah, and Roger Zimmermann. 2018. IceBreaker: Solving Cold Start Problem for Video Recommendation Engines. In 2018 IEEE International Symposium on Multimedia (ISM). IEEE, 217--222.

[15]

Zongxian Li, Sheng Li, Lantian Xue, and Yonghong Tian. 2019. Semi-Siamese Network for Content-Based Video Relevance Prediction. In 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1--5.

[16]

Mengyi Liu, Xiaohui Xie, and Hanning Zhou. 2018. Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline. arXiv preprint arXiv:1806.00737 (2018).

[17]

Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. 2018. A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 6450--6459.

[18]

Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Advances in neural information processing systems. 2643--2651.

Cited By

Zhao HZhang LXu JCai GDong ZWen J(2023)Uncovering User Interest from Biased and Noised Watch Time in Video RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608797(528-539)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608797
Shang YGao CChen JJin DWang MLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Learning Fine-grained User Interests for Micro-video RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591713(433-442)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591713
Ma JBian KWen JXu YZhong MZhu L(2023)SRDPR: Social Relation-driven Dynamic network for Personalized micro-video RecommendationExpert Systems with Applications10.1016/j.eswa.2023.120157226(120157)Online publication date: Sep-2023
https://doi.org/10.1016/j.eswa.2023.120157
Show More Cited By

Index Terms

Overview of Content-Based Click-Through Rate Prediction Challenge for Video Recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

October 2019

2794 pages

ISBN:9781450368896

DOI:10.1145/3343031

General Chairs:
Laurent Amsaleg
CNRS-IRISA, France
,
Benoit Huet
EURECOM, France
,
Martha Larson
Radboud University and TU Delft (Netherlands)
,
Program Chairs:
Guillaume Gravier
CNRS-IRISA, France
,
Hayley Hung
Delft University of Technology Netherlands
,
Chong-Wah Ngo
City University of Hong Kong Hong Kong
,
Wei Tsang Ooi
National University of Singapore Singapore

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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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

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Published: 15 October 2019

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MM '19: The 27th ACM International Conference on Multimedia

October 21 - 25, 2019

Nice, France

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MM '19 Paper Acceptance Rate 252 of 936 submissions, 27%;

Overall Acceptance Rate 995 of 4,171 submissions, 24%

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MM '24

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The 32nd ACM International Conference on Multimedia

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Cited By

Zhao HZhang LXu JCai GDong ZWen J(2023)Uncovering User Interest from Biased and Noised Watch Time in Video RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608797(528-539)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608797
Shang YGao CChen JJin DWang MLi YChen HDuh WHuang HKato MMothe JPoblete B(2023)Learning Fine-grained User Interests for Micro-video RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591713(433-442)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591713
Ma JBian KWen JXu YZhong MZhu L(2023)SRDPR: Social Relation-driven Dynamic network for Personalized micro-video RecommendationExpert Systems with Applications10.1016/j.eswa.2023.120157226(120157)Online publication date: Sep-2023
https://doi.org/10.1016/j.eswa.2023.120157
Ren JZhang R(2023)When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR PredictionAdvanced Data Mining and Applications10.1007/978-3-031-46661-8_48(724-739)Online publication date: 5-Nov-2023
https://doi.org/10.1007/978-3-031-46661-8_48
Zhan RPei CSu QWen JWang XMu GZheng DJiang PGai KZhang ARangwala H(2022)Deconfounding Duration Bias in Watch-time Prediction for Video RecommendationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539092(4472-4481)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539092
Almeida Ade Villiers JDe Freitas AVelayudan M(2021)The complementarity of a diverse range of deep learning features extracted from video content for video recommendationExpert Systems with Applications10.1016/j.eswa.2021.116335(116335)Online publication date: Dec-2021
https://doi.org/10.1016/j.eswa.2021.116335
Jiang HWang WWei YGao ZWang YNie LWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)What Aspect Do You LikeProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413653(3487-3495)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3413653

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