Papers › VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

6 Oct 2015arXiv:1510.01784archive 2025-07-28

Ruining He, Julian McAuley

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.

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abkds/r-ecommender mentioned on GitHubpytorchMIT report
arogers1/VBPR mentioned on GitHub report
domainxz/top-k-rec mentioned on GitHubtf report
ialab-puc/CuratorNet mentioned on GitHubtfMIT report
jchanxtarov/vbpr mentioned on GitHubpytorchMIT report
weiyinwei/huign mentioned on GitHubpytorch report
weiyinwei/mmgcn mentioned on GitHubpytorch report

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Tasks

Multi-modal RecommendationRecommendation Systems

Datasets

Introduced by this paper, per the archive.

Amazon Men

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-modal Recommendation Amazon Baby VBPR NDCG@20 0.0284 #9 of 10 Archive leaderboard report
Multi-modal Recommendation Amazon Clothing VBPR NDCG@20 0.0192 #9 of 10 Archive leaderboard report
Multi-modal Recommendation Amazon Sports VBPR NGCG@20 0.0384 #9 of 10 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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