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Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics.
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Large scale visual recommendations from street fashion images
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Neuroaesthetics in fashion: Modeling the perception of fashionability
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Multi-relational matrix factorization using bayesian personalized ranking for social network data
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Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos
Y. Kalantidis, L. Kennedy, and L.-J. Li · 2013
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From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
J. J. McAuley and J. Leskovec · 2013
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Gbpr: Group preference based bayesian personalized ranking for one-class collaborative filtering
W. Pan and L. Chen · 2013
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One-class collaborative filtering with random graphs
U. Paquet and N. Koenigstein · 2013
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Is a picture really worth a thousand words?-on the role of images in e-commerce
W. Di, N. Sundaresan, R. Piramuthu, and A. Bhardwaj · 2014
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E. Simo-Serra, S. Fidler, F. Moreno-Noguer, and R. Urtasun · 2014
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Accelerating t-sne using tree-based algorithms
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Beyond clicks: dwell time for personalization
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Leveraging social connections to improve personalized ranking for collaborative filtering
T. Zhao, J. McAuley, and I. King · 2014
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Vbpr: Visual bayesian personalized ranking from implicit feedback
R. He and J. McAuley · 2015
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J. J. McAuley, C. Targett, Q. Shi, and A. van den Hengel · 2015
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Daily-aware personalized recommendation based on feature-level time series analysis
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