Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering

25th International World Wide Web Conference, WWW 2016

Volumn , Issue , 2016, Pages 507-517

  He, Ruining ^a   McAuley, Julian ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Personalized Ranking; Recommender Systems; Fashion Evolution; Visual Dimensions

Indexed keywords

DEEP NEURAL NETWORKS; NEURAL NETWORKS; RECOMMENDER SYSTEMS; SEMANTICS; WORLD WIDE WEB;

CONVOLUTIONAL NEURAL NETWORK; NON-LINEAR DYNAMICS; PERSONALIZED RANKING; RANKING FUNCTIONS; REAL-WORLD DATASETS; STATE OF THE ART; VISUAL APPEARANCE; VISUAL DIMENSIONS;

COLLABORATIVE FILTERING;

EID: 84995779362     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2872427.2883037     Document Type: Conference Paper

References (40)

1. D.W. Aha, D. Kibler, and M. K. Albert. Instance-based learning algorithms. Machine learning, 1991.
2. R. M. Bell, Y. Koren, and C. Volinsky. The bellkor solution to the netflix prize, 2007.
3. R. Bellman. On the approximation of curves by line segments using dynamic programming. Communications of the ACM, 1961.
4. J. Bennett and S. Lanning. The netflix prize. In KDDCup, 2007.
5. T. Cebrián, M. Planagumà, P. Villegas, and X. Amatriain. Music recommendations with temporal context awareness. In RecSys, 2010.
6. W. Di, N. Sundaresan, R. Piramuthu, and A. Bhardwaj. Is a picture really worth a thousand words?-on the role of images in e-commerce. In WSDM, 2014.
7. Y. Ding and X. Li. Time weight collaborative filtering. In CIKM, 2005.
8. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML, 2014.
9. Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In RecSys, 2011.
10. J. H. Gilkeson and K. Reynolds. Determinants of internet auction success and closing price: An exploratory study. Psychology & Marketing, 2003.
11. A. Goswami, N. Chittar, and C. H. Sung. A study on the impact of product images on user clicks for online shopping. In WWW, 2011.
12. R. He and J. McAuley. Vbpr: Visual bayesian personalized ranking from implicit feedback. CoRR, 2015.
13. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM. IEEE, 2008.
14. V. Jagadeesh, R. Piramuthu, A. Bhardwaj, W. Di, and N. Sundaresan. Large scale visual recommendations from street fashion images. In SIGKDD, 2014.
15. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In MM, 2014.
16. 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.
17. S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, and H.Winnemoeller. Recognizing image style. In BMVC, 2014.
18. R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, 2004.
19. Y. Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 2010.
20. Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender systems handbook. Springer, 2011.
21. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
22. A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In WSDM, 2012.
23. N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In SIGIR, 2010.
24. X. Lu, Z. Lin, H. Jin, J. Yang, and J. Z. Wang. RAPID: rating pictorial aesthetics using deep learning. In MM, 2014.
25. J. J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In WWW, 2013.
26. J. J. McAuley, C. Targett, Q. Shi, and A. van den Hengel. Image-based recommendations on styles and substitutes. In SIGIR, 2015.
27. R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In ICDM, 2008.
28. W. Pan and L. Chen. Gbpr: Group preference based bayesian personalized ranking for one-class collaborative filtering. In IJCAI, 2013.
29. U. Paquet and N. Koenigstein. One-class collaborative filtering with random graphs. In WWW, 2013.
30. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.
31. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and L. Fei-Fei. Imagenet large scale visual recognition challenge. CoRR, 2014.
32. E. Simo-Serra, S. Fidler, F. Moreno-Noguer, and R. Urtasun. Neuroaesthetics in fashion: Modeling the perception of fashionability. In CVPR, 2014.
33. D. H. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In WWW, 2009.
34. A. Tsymbal. The problem of concept drift: definitions and related work. Technical Report, 2004.
35. L. van der Maaten. Accelerating t-sne using tree-based algorithms. Journal of machine learning research, 2014.
36. A. Veit, B. Kovacs, S. Bell, J. McAuley, K. Bala, and S. Belongie. Learning visual clothing style with heterogeneous dyadic co-occurrences. In ICCV, 2015.
37. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining conceptdrifting data streams using ensemble classifiers. In SIGKDD, 2003.
38. X. Yi, L. Hong, E. Zhong, N. N. Liu, and S. Rajan. Beyond clicks: dwell time for personalization. In RecSys, 2014.
39. 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, 2015.
40. T. Zhao, J. McAuley, and I. King. Leveraging social connections to improve personalized ranking for collaborative filtering. In CIKM, 2014.