research-article

Visual Background Recommendation for Dance Performances Using Deep Matrix Factorization

Authors:

Hui MaoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 14, Issue 1

Article No.: 11, Pages 1 - 19

https://doi.org/10.1145/3152463

Published: 16 January 2018 Publication History

Abstract

The stage background is one of the most important features for a dance performance, as it helps to create the scene and atmosphere. In conventional dance performances, the background images are usually selected or designed by professional stage designers according to the theme and the style of the dance. In new media dance performances, the stage effects are usually generated by media editing software. Selecting or producing a dance background is quite challenging and is generally carried out by skilled technicians. The goal of the research reported in this article is to ease this process. Instead of searching for background images from the sea of available resources, dancers are recommended images that they are more likely to use. This work proposes the idea of a novel system to recommend images based on content-based social computing. The core part of the system is a probabilistic prediction model to predict a dancer’s interests in candidate images through social platforms. Different from traditional collaborative filtering or content-based models, the model proposed here effectively combines a dancer’s social behaviors (rating action, click action, etc.) with the visual content of images shared by the dancer using deep matrix factorization (DMF). With the help of such a system, dancers can select from the recommended images and set them as the backgrounds of their dance performances through a media editor. According to the experiment results, the proposed DMF model outperforms the previous methods, and when the dataset is very sparse, the proposed DMF model shows more significant results.

References

[1]

Gediminas Adomavicius and Alexander 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 17, 6, 734--749.

Digital Library

[2]

Ryan Aylward and Joseph A. Paradiso. 2006. Sensemble: A wireless, compact, multi-user sensor system for interactive dance. In Proceedings of the 2006 Conference on New Interfaces for Musical Expression. 134--139.

[3]

Allison J. B. Chaney, David M. Blei, and Tina Eliassi-Rad. 2015. A probabilistic model for using social networks in personalized item recommendation. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, New York, NY, 43--50.

Digital Library

[4]

Alexis Clay, Nadine Couture, Laurence Nigay, Jean-Baptiste De La Riviere, Jean-Claude Martin, Matthieu Courgeon, Myriam Desainte-Catherine, Emmanuel Orvain, Vincent Girondel, and Gaël Domengero. 2012. Interactions and systems for augmenting a live dance performance. In Proceedings of the IEEE International Symposium on Mixed and Augmented Reality. IEEE, Los Alamitos, CA, 29--38.

[5]

Alexis Clay, Gal Domenger, Julien Conan, Axel Domenger, and Nadine Couture. 2014. Integrating augmented reality to enhance expression, interaction and collaboration in live performances: A ballet dance case study. In Proceedings of the IEEE International Symposium on Mixed and Augmented Reality-Media, Art, Social Science, Humanities, and Design. IEEE, Los Alamitos, CA, 21--29.

[6]

Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, vol. 60.

[7]

Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web. ACM, New York, NY, 271--280.

Digital Library

[8]

Chen Fang, Hailin Jin, Jianchao Yang, and Zhe Lin. 2015. Collaborative feature learning from social media. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 577--585.

[9]

Leon Gatys, Alexander S. Ecker, and Matthias Bethge. 2015. Texture synthesis using convolutional neural networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15). 262--270.

[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]

Yaya Heryadi, Mohamad Ivan Fanany, and Aniati Murni Arymurthy. 2012. A syntactical modeling and classification for performance evaluation of bali traditional dance. In Proceedings of the 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS’12). IEEE, Los Alamitos, CA, 261--265.

[12]

Jodi James, Todd Ingalls, Gang Qian, Loren Olsen, Daniel Whiteley, Siew Wong, and Thanassis Rikakis. 2006. Movement-based interactive dance performance. In Proceedings of the 14th ACM International Conference on Multimedia. ACM, New York, NY, 470--480.

Digital Library

[13]

Celine Latulipe, David Wilson, Sybil Huskey, Berto Gonzalez, and Melissa Word. 2011. Temporal integration of interactive technology in dance: Creative process impacts. In Proceedings of the 8th ACM International Conference on Creativity and Cognition. ACM, New York, NY, 107--116.

Digital Library

[14]

Celine Latulipe, David Wilson, Sybil Huskey, Melissa Word, Arthur Carroll, Erin Carroll, Berto Gonzalez, Vikash Singh, Mike Wirth, and Danielle Lottridge. 2010. Exploring the design space in technology-augmented dance. In CHI’10 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, 2995--3000.

[15]

Xiaopeng Li, Ming Cheung, and James She. 2016. Connection discovery using shared images by Gaussian relational topic model. In Proceedings of the IEEE International Conference on Big Data. IEEE, Los Alamitos, CA, 931--936.

[16]

Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 203--210.

[17]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 2579--2605.

[18]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the ACM International Conference on Recommender Systems. ACM, New York, NY, 17--24.

Digital Library

[19]

Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. 2002. Content-boosted collaborative filtering for improved recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence. 187--192.

[20]

Chulsung Park, Pai H. Chou, and Yicun Sun. 2006. A wearable wireless sensor platform for interactive dance performances. In Proceedings of 4th Annual IEEE International Conference on Pervasive Computing and Communications. IEEE, Los Alamitos, CA.

Digital Library

[21]

Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). 1257--1264.

Digital Library

[22]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, New York, NY, 285--295.

Digital Library

[23]

Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations.

[24]

Ian Soboroff and Charles Nicholas. 1999. Combining content and collaboration in text filtering. In Proceedings of the International Joint Conference on Artificial Intelligence, vol. 99. 86--91.

[25]

Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS’13), Vol. 2. 2643--2651.

[26]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 1235--1244.

Digital Library

[27]

Jun Wang, Arjen P. De Vries, and Marcel J. T. Reinders. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 501--508.

[28]

Xinxi Wang and Ye Wang. 2014. Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM International Conference on Multimedia. ACM, New York, NY, 627--636.

Digital Library

[29]

Jiqing Wen, Xiaopeng Li, James She, Soochang Park, and Ming Cheung. 2016. Visual background recommendation for dance performances using dancer-shared images. In Proceedings of the IEEE International Conference on Cyber, Physical, and Social Computing. IEEE, Los Alamitos, CA, 521--527.

[30]

Longqi Yang, Cheng-Kang Hsieh, and Deborah Estrin. 2015. Beyond classification: Latent user interests profiling from visual contents analysis. In Proceedings of the IEEE International Conference on Data Mining Workshop. IEEE, Los Alamitos, CA, 1410--1416.

Digital Library

Cited By

Yang MXu S(2022)Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network▪Knowledge-Based Systems10.1016/j.knosys.2021.107236227:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107236
Bobadilla JOrtega FGutiérrez AGonzález-Prieto Á(2022)Deep variational models for collaborative filtering-based recommender systemsNeural Computing and Applications10.1007/s00521-022-08088-235:10(7817-7831)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1007/s00521-022-08088-2
Yoon HLi SSung Y(2021)Style Transformation Method of Stage Background Images by Emotion Words of LyricsMathematics10.3390/math91518319:15(1831)Online publication date: 3-Aug-2021
https://doi.org/10.3390/math9151831
Show More Cited By

Index Terms

Visual Background Recommendation for Dance Performances Using Deep Matrix Factorization
1. Applied computing
  1. Arts and humanities
    1. Performing arts
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media
      2. Social recommendation

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 14, Issue 1

February 2018

287 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3173554

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2018 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 January 2018

Accepted: 01 October 2017

Revised: 01 October 2017

Received: 01 July 2017

Published in TOMM Volume 14, Issue 1

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Cited By

Yang MXu S(2022)Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network▪Knowledge-Based Systems10.1016/j.knosys.2021.107236227:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107236
Bobadilla JOrtega FGutiérrez AGonzález-Prieto Á(2022)Deep variational models for collaborative filtering-based recommender systemsNeural Computing and Applications10.1007/s00521-022-08088-235:10(7817-7831)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1007/s00521-022-08088-2
Yoon HLi SSung Y(2021)Style Transformation Method of Stage Background Images by Emotion Words of LyricsMathematics10.3390/math91518319:15(1831)Online publication date: 3-Aug-2021
https://doi.org/10.3390/math9151831
Deldjoo YSchedl MHidasi BWei YHe X(2021)Multimedia Recommender Systems: Algorithms and ChallengesRecommender Systems Handbook10.1007/978-1-0716-2197-4_25(973-1014)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_25
Deldjoo YSchedl MCremonesi PPasi G(2020)Recommender Systems Leveraging Multimedia ContentACM Computing Surveys10.1145/340719053:5(1-38)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3407190
Mittal TBhattacharya UChandra RBera AManocha DWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Emotions Don't LieProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413570(2823-2832)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3413570
Zhou XTan W(2020)An Improved Collaborative Filtering Algorithm Based on Filling Missing DataHuman Centered Computing10.1007/978-3-030-70626-5_23(220-226)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-70626-5_23
Kniaz VKnyaz VRemondino FWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)The point where reality meets fantasyProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454307(215-226)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454307
Feng ZYu ZJing YWu SSong MYang YJiang J(2019)Interpretable Partitioned Embedding for Intelligent Multi-item Fashion Outfit CompositionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332633215:2s(1-20)Online publication date: 29-Jul-2019
https://dl.acm.org/doi/10.1145/3326332
Zhang WZhang XWang HChen D(2019)A deep variational matrix factorization method for recommendation on large scale sparse datasetNeurocomputing10.1016/j.neucom.2019.01.028Online publication date: Jan-2019
https://doi.org/10.1016/j.neucom.2019.01.028

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