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Video eCommerce: Towards Online Video Advertising

Published: 01 October 2016 Publication History

Abstract

The prevalence of online videos provides an opportunity for e-commerce companies to exhibit their product ads in videos by recommendation. In this paper, we propose an advertising system named Video eCommerce to exhibit appropriate product ads to particular users at proper time stamps of videos, which takes into account video semantics, user shopping preference and viewing behavior feedback by a two-level strategy. At the first level, Co-Relation Regression (CRR) model is novelly proposed to construct the semantic association between keyframes and products. Heterogeneous information network (HIN) is adopted to build the user shopping preference from two different e-commerce platforms, Tmall and MagicBox, which alleviates the problems of data sparsity and cold start. In addition, Video Scene Importance Model (VSIM) utilizes the viewing behavior of users to embed ads at the most attractive position within the video stream. At the second level, taking the results of CRR, HIN and VSIM as the input, Heterogeneous Relation Matrix Factorization (HRMF) is applied for product advertising. Extensive evaluation on a variety of online videos from Tmall MagicBox demonstrates that Video eCommerce achieves promising performance, which significantly outperforms the state-of-the-art advertising methods.

References

[1]
G. Aggarwal, J. Feldman, S. Muthukrishnan, and M. Pál. Sponsored search auctions with markovian users. In Internet and Network Economics, pages 621--628. 2008.
[2]
D. M. Chickering and D. Heckerman. Targeted advertising on the web with inventory management. Interfaces, 33(5):71--77, 2003.
[3]
P. Cui, Z. Wang, and Z. Su. What videos are similar with you?: Learning a common attributed representation for video recommendation. In ACM MM, pages 597--606, 2014.
[4]
K. Eyal and A. M. Rubin. Viewer aggression and homophily, identification, and parasocial relationships with television characters. Journal of Broadcasting & Electronic Media, 47(1):77--98, 2003.
[5]
J. Hoffman, S. Guadarrama, E. S. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko. Lsda: Large scale detection through adaptation. In NIPS, pages 3536--3544, 2014.
[6]
S. Isaacman, S. Ioannidis, A. Chaintreau, and M. Martonosi. Distributed rating prediction in user generated content streams. In RecSys, pages 69--76, 2011.
[7]
P. Jiang, Y. Zhu, Y. Zhang, and Q. Yuan. Life-stage prediction for product recommendation in e-commerce. In KDD, pages 1879--1888, 2015.
[8]
W. Kar, V. Swaminathan, and P. Albuquerque. Selection and ordering of linear online video ads. In RecSys, pages 203--210, 2015.
[9]
D. Kempe and M. Mahdian. A cascade model for externalities in sponsored search. In Internet and Network Economics, pages 585--596. 2008.
[10]
Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.
[11]
H. Ma. On measuring social friend interest similarities in recommender systems. In SIGIR, pages 465--474, 2014.
[12]
J. J. McAuley, R. Pandey, and J. Leskovec. Inferring networks of substitutable and complementary products. In KDD, pages 785--794, 2015.
[13]
T. Mei and X.-S. Hua. Contextual internet multimedia advertising. Proceedings of the IEEE, 98(8):1416--1433, 2010.
[14]
T. Mei, X.-S. Hua, and S. Li. Contextual in-image advertising. In ACM MM, pages 439--448, 2008.
[15]
T. Mei, X.-S. Hua, and S. Li. Videosense: A contextual in-video advertising system. Circuits and Systems for Video Technology, IEEE Transactions on, 19(12):1866--1879, 2009.
[16]
T. Mei, L. Li, X.-S. Hua, and S. Li. Imagesense: towards contextual image advertising. ACM TOMM, 8(1):6, 2012.
[17]
G. Roels and K. Fridgeirsdottir. Dynamic revenue management for online display advertising. Journal of Revenue & Pricing Management, 8(5):452--466, 2009.
[18]
I. Ronen, I. Guy, E. Kravi, and M. Barnea. Recommending social media content to community owners. In SIGIR, pages 243--252, 2014.
[19]
R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2007.
[20]
S. Sarawagi. Information extraction. Foundations and trends in databases, 1(3):261--377, 2008.
[21]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001.
[22]
Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. PVLDB, 4(11):992--1003, 2011.
[23]
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. arXiv:1409.4842, 2014.
[24]
J. Turner, A. Scheller-Wolf, and S. Tayur. Or practice-scheduling of dynamic in-game advertising. Operations research, 59(1):1--16, 2011.
[25]
J. Wang, A. P. De Vries, and M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR, pages 501--508, 2006.
[26]
X. Wu, A. G. Hauptmann, and C.-W. Ngo. Practical elimination of near-duplicates from web video search. In ACM MM, pages 218--227, 2007.
[27]
B. Yang, Y. Lei, D. Liu, and J. Liu. Social collaborative filtering by trust. In AAAI, pages 2747--2753, 2013.
[28]
X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. Personalized entity recommendation: A heterogeneous information network approach. In WSDM, pages 283--292, 2014.
[29]
S. Zhang, W. Wang, J. Ford, and F. Makedon. Learning from incomplete ratings using non-negative matrix factorization. In SDM, volume 6, pages 548--552, 2006.
[30]
Y. Zhang, M. Zhang, Y. Zhang, G. Lai, Y. Liu, H. Zhang, and S. Ma. Daily-aware personalized recommendation based on feature-level time series analysis. In WWW, pages 1373--1383, 2015.
[31]
W. Zhong, R. Jin, C. Yang, X. Yan, Q. Zhang, and Q. Li. Stock constrained recommendation in tmall. In KDD, pages 2287--2296, 2015.
[32]
T. Zhu, P. Harrington, J. Li, and L. Tang. Bundle recommendation in ecommerce. In SIGIR, pages 657--666, 2014.

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cover image ACM Conferences
MM '16: Proceedings of the 24th ACM international conference on Multimedia
October 2016
1542 pages
ISBN:9781450336031
DOI:10.1145/2964284
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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Publication History

Published: 01 October 2016

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Author Tags

  1. e-commerce
  2. online advertising
  3. video analysis

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MM '16
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MM '16: ACM Multimedia Conference
October 15 - 19, 2016
Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2024)Multi-Task Paired Masking With Alignment Modeling for Medical Vision-Language Pre-TrainingIEEE Transactions on Multimedia10.1109/TMM.2023.332596526(4706-4721)Online publication date: 1-Jan-2024
  • (2024)When Channel Correlation Meets Sparse Prior: Keeping Interpretability in Image Compressive SensingIEEE Transactions on Multimedia10.1109/TMM.2023.330582826(2953-2965)Online publication date: 1-Jan-2024
  • (2024)Automatic Generation of Interactive Nonlinear Video for Online Apparel Shopping NavigationIEEE Transactions on Multimedia10.1109/TMM.2023.326661526(474-486)Online publication date: 1-Jan-2024
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