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In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Factorization machines, icdm, 2010
Rendle, S · 2010
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L · 2012
Earlier work this paper cites.
Distributed representations of sentences and documents
Le, Q., and Mikolov, T · 2014
Earlier work this paper cites.
Vbpr: visual bayesian personalized ranking from implicit feedback
He, R., and McAuley, J · 2015
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Visually-aware fashion recommendation and design with generative image models
Kang, W.-C., Fang, C., Wang, Z., and McAuley, J · 2017
Cited alongside, same era.
Deepstyle: Learning user preferences for visual recommendation
Liu, Q., Wu, S., and Wang, L · 2017
Cited alongside, same era.
Joint representation learning for top-n recommendation with heterogeneous information sources
Zhang, Y., Ai, Q., Chen, X., and Croft, W. B · 2017
Cited alongside, same era.
Wsabie: Scaling up to large vocabulary image annotation
Weston, J., Bengio, S., and Usunier, N
Cited in the paper.
Collective embedding for neural context-aware recommender systems
Costa, F. S. d., and Dolog, P · 2019
Later among the works it cites.
Explainable fashion recommendation: A semantic attribute region guided approach
Hou, M., Wu, L., Chen, E., Li, Z., Zheng, V. W., and Liu, Q · 2019
Later among the works it cites.
Attribute-aware non-linear co-embeddings of graph features
Rashed, A., Grabocka, J., and Schmidt-Thieme, L · 2019
Later among the works it cites.
On sampled metrics for item recommendation
Krichene, W., and Rendle, S · 2020
Later among the works it cites.
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