VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

TL;DR

This paper introduces a scalable recommendation model that incorporates visual features from product images, improving personalized ranking accuracy and addressing cold start problems by uncovering visual dimensions influencing user preferences.

Contribution

It presents a novel factorization model that integrates deep visual features into personalized ranking, enhancing recommendation quality and interpretability.

Findings

Improved ranking accuracy on large datasets

Enhanced cold start recommendation performance

Qualitative analysis of visual dimensions influencing preferences

Abstract

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. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.