108 BIBLIOGRAPHY
[50] Rongrong Ji, Xing Xie, Hongxun Yao, and Wei-Ying Ma. Mining city landmarks from
blogs by graph modeling. In Proc. of the ACM International Conference on Multimedia,
pages 105–114, 2009. DOI: 10.1145/1631272.1631289 19
[51] Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross
Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional architecture
for fast feature embedding. In Proc. of the ACM International Conference on Multimedia,
pages 675–678, 2014. DOI: 10.1145/2647868.2654889 18, 36
[52] Lu Jiang, Shoou-I Yu, Deyu Meng, Yi Yang, Teruko Mitamura, and Alexander G.
Hauptmann. Fast and accurate content-based semantic search in 100 m internet videos.
In Proc. of the ACM International Conference on Multimedia, pages 49–58, 2015. DOI:
10.1145/2733373.2806237 25, 41, 61, 76
[53] Shuhui Jiang, Yue Wu, and Yun Fu. Deep bi-directional cross-triplet embedding for
cross-domain clothing retrieval. In ACM Conference on Multimedia Conference, pages 52–
56, 2016. DOI: 10.1145/2964284.2967182 88
[54] Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. Visually-aware
fashion recommendation and design with generative image models. In IEEE Interna-
tional Conference on Data Mining, pages 207–216, 2017. DOI: 10.1109/icdm.2017.30 79
[55] Aditya Khosla, Atish Das Sarma, and Raffay Hamid. What makes an image popu-
lar? In Proc. of the ACM International WWW Conference, pages 867–876, 2014. DOI:
10.1145/2566486.2567996 18, 70
[56] Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. Con-
volutional matrix factorization for document context-aware recommendation. In
Proc. of the ACM Conference on Recommender Systems, pages 233–240, 2016. DOI:
10.1145/2959100.2959165 48, 68
[57] Yoon Kim. Convolutional neural networks for sentence classification. In Proc. of the
Conference on Empirical Methods in Natural Language Processing, pages 1746–1751, 2014.
DOI: 10.3115/v1/d14-1181 36, 70
[58] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. ArXiv
Preprint ArXiv:1412.6980, 2014. 71
[59] Yehuda Koren. Factorization meets the neighborhood: A multifaceted collaborative fil-
tering model. In Proc. of the International ACM SIGKDD Conference, pages 426–434,
2008. DOI: 10.1145/1401890.1401944 25
[60] Yehuda Koren and Robert Bell. Advances in collaborative filtering. Recommender Systems
Handbook, pages 77–118, 2015. DOI: 10.1007/978-1-4899-7637-6_3 68
BIBLIOGRAPHY 109
[61] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for
recommender systems. IEEE Computer, vol. 42, no. 8, pages 30–37, 2009. DOI:
10.1109/mc.2009.263 48
[62] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with
deep convolutional neural networks. In Proc. of the Advances in Neural Information Pro-
cessing Systems, pages 1097–1105, 2012. DOI: 10.1145/3065386 14, 55
[63] Ranjitha Kumar and Kristen Vaccaro. An experimentation engine for data-driven fashion
systems. In AAAI Spring Symposium Series, 2017. 79
[64] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature,
521(7553):436–444, 2015. DOI: 10.1038/nature14539 14
[65] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-
negative matrix factorization. Nature Publishing Group, vol. 401, page 788, 1999. DOI:
10.1038/44565 46, 48
[66] Xuelong Li, Guosheng Cui, and Yongsheng Dong. Graph regularized non-negative low-
rank matrix factorization for image clustering. IEEE, vol. 47, pages 3840–3853, 2017.
DOI: 10.1109/tcyb.2016.2585355 51
[67] Yuncheng Li, Liangliang Cao, Jiang Zhu, and Jiebo Luo. Mining fashion outfit com-
position using an end-to-end deep learning approach on set data. IEEE Transactions on
Multimedia, 19(8):1946–1955, 2017. DOI: 10.1109/tmm.2017.2690144 13, 14, 28, 47,
65, 82
[68] Jian Han Lim, Nurul Japar, Chun Chet Ng, and Chee Seng Chan. Unprecedented usage
of pre-trained CNNs on beauty product. In Proc. of the ACM International Conference on
Multimedia, pages 2068–2072, 2018. DOI: 10.1145/3240508.3266433 70
[69] Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Ri-
jke. Explainable fashion recommendation with joint outfit matching and comment
generation. IEEE Transactions on Knowledge and Data Engineering, 2018. DOI:
10.1109/tkde.2019.2906190 47
[70] Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Ri-
jke. Improving outfit recommendation with co-supervision of fashion generation. In
International World Wide Web Conference, 2019. DOI: 10.1145/3308558.3313614 82
[71] Jingyuan Liu and Hong Lu. Deep fashion analysis with feature map upsampling
and landmark-driven attention. In European Conference on Computer Vision Workshops,
pages 30–36, 2018. DOI: 10.1007/978-3-030-11015-4_4 82
110 BIBLIOGRAPHY
[72] Meng Liu, Liqiang Nie, Meng Wang, and Baoquan Chen. Towards micro-video under-
standing by joint sequential-sparse modeling. In Proc. of the ACM International Conference
on Multimedia, pages 970–978, 2017. DOI: 10.1145/3123266.3123341 70
[73] Meng Liu, Xiang Wang, Liqiang Nie, Xiangnan He, Baoquan Chen, and Tat-Seng
Chua. Attentive moment retrieval in videos. In Proc. of the International ACM SIGIR Con-
ference on Research and Development in Information Retrieval, pages 15–24, 2018. DOI:
10.1145/3209978.3210003 50
[74] Meng Liu, Xiang Wang, Liqiang Nie, Qi Tian, Baoquan Chen, and Tat-Seng Chua.
Cross-modal moment localization in videos. In Proc. of the ACM International Conference
on Multimedia, pages 843–851, 2018. DOI: 10.1145/3240508.3240549 67
[75] Meng Liu, Liqiang Nie, Xiang Wang, Qi Tian, and Baoquan Chen. Online data or-
ganizer: Micro-video categorization by structure-guided multimodal dictionary learning.
IEEE, vol. 28, pages 1235–1247, 2019. DOI: 10.1109/tip.2018.2875363 50
[76] Si Liu, Jiashi Feng, Zheng Song, Tianzhu Zhang, Hanqing Lu, Changsheng Xu, and
Shuicheng Yan. Hi, magic closet, tell me what to wear! In Proc. of the ACM International
Conference on Multimedia, pages 619–628, 2012. DOI: 10.1145/2393347.2393433 3, 7,
13
[77] Si Liu, Zheng Song, Guangcan Liu, Changsheng Xu, Hanqing Lu, and Shuicheng Yan.
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set.
In Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition,
pages 3330–3337, 2012. DOI: 10.1109/cvpr.2012.6248071 13
[78] Siyuan Liu, Qiong Wu, and Chunyan Miao. Personalized recommendation consider-
ing secondary implicit feedback. In Proc. of the IEEE International Conference on Agents,
pages 87–92, 2018. DOI: 10.1109/agents.2018.8460053 68
[79] Xin Liu, An Li, Ji-Xiang Du, Shu-Juan Peng, and Wentao Fan. Efficient cross-modal re-
trieval via flexible supervised collective matrix factorization hashing, Multimedia Tools and
Applications, vol. 77, no. 21, pages 28665–28683, Springer, 2018. DOI: 10.1007/s11042-
018-6006-5 48
[80] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion:
Powering robust clothes recognition and retrieval with rich annotations. In IEEE
Conference on Computer Vision and Pattern Recognition, pages 1096–1104, 2016. DOI:
10.1109/cvpr.2016.124 88
[81] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. Deepfashion: Pow-
ering robust clothes recognition and retrieval with rich annotations. In Proc. of IEEE
BIBLIOGRAPHY 111
Conference on Computer Vision and Pattern Recognition, pages 1096–1104, 2016. DOI:
10.1109/cvpr.2016.124 55
[82] Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. Bayesian personalized
ranking with multi-channel user feedback. In Proc. of the ACM Conference on Recommender
Systems, pages 361–364, 2016. DOI: 10.1145/2959100.2959163 68
[83] Maryam Ziaeefard, Jaime Camacaro, and Carolina Bessega. Hierarchical feature map
characterization in fashion interpretation. In Conference on Computer and Robot Vision,
pages 88–94, 2018. DOI: 10.1109/crv.2018.00022 82
[84] Yihui Ma, Jia Jia, Suping Zhou, Jingtian Fu, Yejun Liu, and Zijian Tong. Towards better
understanding the clothing fashion styles: A multimodal deep learning approach. In Proc.
of the International Joint Conference on Artificial Intelligence, pages 38–44, AAAI Press,
2017. 34
[85] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of
Machine Learning Research, 9:2579–2605, 2008. 92
[86] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. Image-
based recommendations on styles and substitutes. In Proc. of the International ACM SI-
GIR Conference on Research and Development in Information Retrieval, pages 43–52, 2015.
DOI: 10.1145/2766462.2767755 7, 13, 21, 28, 37, 47, 57
[87] Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel.
Image-based recommendations on styles and substitutes. In ACM SIGIR Confer-
ence on Research and Development in Information Retrieval, pages 43–52, 2015. DOI:
10.1145/2766462.2767755 9, 82
[88] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv
preprint arXiv:1411.1784, 2014. 101
[89] Andriy Mnih and Ruslan R. Salakhutdinov. Probabilistic matrix factorization. In Ad-
vances in Neural Information Processing Systems, pages 1257–1264, 2008. 48
[90] Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y.
Ng. Multimodal deep learning. In Proc. of the International Conference on Machine Learn-
ing, pages 689–696, JMLR.org, 2011. 14
[91] Liqiang Nie, Xuemeng Song, and Tat-Seng Chua. Learning from Multiple Social Net-
works. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan &
Claypool Publishers, 2016. DOI: 10.2200/s00714ed1v01y201603icr048 81
112 BIBLIOGRAPHY
[92] Charles Packer, Julian McAuley, and Arnau Ramisa. Visually-aware personalized recom-
mendation using interpretable image representations, ArXiv Preprint ArXiv:1806.09820,
2018. 68
[93] Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz,
and Qiang Yang. One-class collaborative filtering. In International Conference on Data
Mining, pages 502–511, 2008. DOI: 10.1109/icdm.2008.16 81
[94] Xueming Qian, He Feng, Guoshuai Zhao, and Tao Mei. Personalized recommenda-
tion combining user interest and social circle. IEEE Transactions on Knowledge and Data
Engineering, 26(7):1763–1777, 2014. DOI: 10.1109/tkde.2013.168 12, 21, 37
[95] Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, and Jiawei Han. An
attention-based collaboration framework for multi-view network representation learning.
In Proc. of the ACM International Conference on Information and Knowledge Management,
pages 1767–1776, 2017. DOI: 10.1145/3132847.3133021 33
[96] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learn-
ing with deep convolutional generative adversarial networks. International Conference on
Learning Representations, ArXiv Preprint ArXiv:1511.06434, 2016. 101
[97] Dimitrios Rafailidis and Fabio Crestani. Cluster-based joint matrix factorization
hashing for cross-modal retrieval. In Proc. of the International ACM SIGIR Confer-
ence on Research and Development in Information Retrieval, pages 781–784, 2016. DOI:
10.1145/2911451.2914710 48
[98] Janarthanan Rajendran, Mitesh M. Khapra, Sarath Chandar, and Balaraman Ravindran.
Bridge correlational neural networks for multilingual multimodal representation learning.
arXiv preprint arXiv:1510.03519, 2015. DOI: 10.18653/v1/n16-1021 14, 30
[99] Steffen Rendle and Lars Schmidt-ieme. Pairwise interaction tensor factorization for
personalized tag recommendation. In Proc. of the ACM International WSDM Conference,
pages 81–90, 2010. DOI: 10.1145/1718487.1718498 19, 57
[100] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-ieme.
BPR: Bayesian personalized ranking from implicit feedback. In Proc. of the International
Conference on Uncertainty in Artificial Intelligence, pages 452–461, AUAI Press, 2009. 12,
17, 28, 31, 47, 50, 64, 68, 71, 81, 84
[101] Hosnieh Sattar, Gerard Pons-Moll, and Mario Fritz. Fashion is taking shape: Un-
derstanding clothing preference based on body shape from online sources. In IEEE
Winter Conference on Applications of Computer Vision, pages 968–977, 2019. DOI:
10.1109/wacv.2019.00108 79, 82
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