Image-based recommendations on styles and substitutes

SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2015, Pages 43-52

  McAuley, Julian ^a   Targett, Christopher ^b   Shi, Qinfeng ^b   Van Den Hengel, Anton ^b,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF ADELAIDE   (Australia)

^c AUSTRALIAN CENTRE FOR ROBOTIC VISION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

SCALABILITY;

FINE GRAINED; HUMAN SENSE; IMAGE-BASED; LARGE-SCALE DATASET; NETWORK INFERENCE; OBJECTS-BASED; SCALABLE METHODS; USER ANNOTATIONS;

INFORMATION RETRIEVAL;

EID: 84953807236     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2766462.2767755     Document Type: Conference Paper

References (41)

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDD, 2005.
2. G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos. Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. on PAMI, 2007.
3. W. Chu and S.-T. Park. Personalized recommendation on dynamic content using predictive bilinear models. In WWW, 2009.
4. M. der and L. Saul. Latent coincidence analysis: A hidden variable model for distance metric learning. In NIPS, 2012.
5. W. Di, C. Wah, A. Bhardwaj, R. Piramuthu, and N. Sundaresan. Style finder: Fine-grained clothing style detection and retrieval. In CVPR Workshops, 2013.
6. C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros. What makes paris look like paris? SIGGRAPH, 2012.
7. J. Hays and A. A. Efros. Im2gps: estimating geographic information from a single image. In CVPR, 2008.
8. M. D. Hoffman, D. M. Blei, and F. Bach. Online learning for latent dirichlet allocation. In In NIPS, 2010.
9. D. J. Hu, R. Hall, and J. Attenberg. Style in the long tail: Discovering unique interests with latent variable models in large scale social e-commerce. In KDD, 2014.
10. V. Jagadeesh, R. Piramuthu, A. Bhardwaj, W. Di, and N. Sundaresan. Large scale visual recommendations from street fashion images. arXiv:1401.1778, 2014.
11. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093, 2014.
12. X. Jin, J. Luo, J. Yu, G. Wang, D. Joshi, and J. Han. Reinforced similarity integration in image-rich information networks. IEEE Trans. on KDE, 2013.
13. Y. Kalantidis, L. Kennedy, and L.-J. Li. Getting the look: Clothing recognition and segmentation for automatic product suggestions in everyday photos. In ICMR, 2013.
14. M. H. Kiapour, K. Yamaguchi, A. C. Berg, and T. L. Berg. Hipster wars: Discovering elements of fashion styles. In ECCV. 2014.
15. W. Kong, W.-J. Li, and M. Guo. Manhattan hashing for large-scale image retrieval. In SIGIR, 2012.
16. Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook. Springer, 2011.
17. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.
18. M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. ACM TOMCCAP, 2006.
19. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 2003.
20. S. Liu, J. Feng, Z. Song, T. Zhang, H. Lu, C. Xu, and S. Yan. Hi, magic closet, tell me what to wear! In ACM Conference on Multimedia, 2012.
21. Y. Liu, W. Wang, B. Lévy, F. Sun, D.-M. Yan, L. Lu, and C. Yang. On centroidal Voronoi tessellation - energy smoothness and fast computation. ACM Trans. on Graphics, 2009.
22. A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV. 2008.
23. A. Mas-Colell, M. Whinston, and J. Green. Microeconomic Theory. Oxford University Press, 1995.
24. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In AAAI, 2002.
25. A. C. Murillo, I. S. Kwak, L. Bourdev, D. Kriegman, and S. Belongie. Urban tribes: Analyzing group photos from a social perspective. In CVPR Workshops, 2012.
26. Y. Pan, T. Yao, T. Mei, L. Houqiang, C.-W. Ngo, and Y. Rui. Click-through-based cross-view learning for image search. In SIGIR, 2014.
27. P. Peng, L. Shou, K. Chen, G. Chen, and S. Wu. The knowing camera 2: Recognizing and annotating places-of-interest in smartphone photos. In SIGIR, 2014.
28. A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. In SIGIR, 2002.
29. B.-S. Seah, S. Bhowmick, and A. Sun. Prism: Concept-preserving social image search results summarization. In SIGIR, 2014.
30. A. Shrivastava, T. Malisiewicz, A. Gupta, and A. A. Efros. Data-driven visual similarity for cross-domain image matching. ACM Trans. on Graphics, 2011.
31. M. Slaney, K. Weinberger, and W. White. Learning a metric for music similarity. In International Conference of Music Information Retrieval, 2008.
32. A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. on PAMI, 2000.
33. D. Spina and J. Gonzalo. Learning similarity functions for topic detection in online reputation monitoring. In SIGIR, 2014.
34. A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Trans. on PAMI, 2008.
35. L. Torresani and K. Chih Lee. Large margin component analysis. In NIPS, 2007.
36. M. Trevisiol, L. Chiarandini, L. Maria Aiello, and A. Jaimes. Image ranking based on user browsing behavior. In SIGIR, 2012.
37. C.-C. Wu, T. Mei, W. Hsu, and Y. Rui. Learning to personalize trending image search suggestion. In SIGIR, 2014.
38. H. Xia, P. Wu, S. Hoi, and R. Jin. Boosting multi-kernel locality-sensitive hashing for scalable image retrieval. In SIGIR, 2012.
39. K. Xu, H. Li, H. Zhang, D. Cohen-Or, Y. Xiong, and Z.-Q. Cheng. Style-content separation by anisotropic part scales. ACM Trans. on Graphics, 2010.
40. K. Yamaguchi, M. H. Kiapour, and T. L. Berg. Paper doll parsing: Retrieving similar styles to parse clothing items. In ICCV, 2013.
41. K. Zhou, S.-H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In SIGIR, 2011.