VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

Understand

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 product attributes, temporal information, or review text.
• However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered.
• In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets.