VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as pr
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http://cseweb.ucsd.edu/~jmcauley/pdfs/aaai16.pdf
