mlsanityData to 7 Oct 2026

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

arXiv 1510.01784 ↗First posted 6 Oct 2015cs.IR cs.AIcited by 151

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Abstract (arXiv)

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.

Key facts. Cited by 151 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2015.

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Citing papers per year: 2021: 16; 2022: 13; 2023: 21; 2024: 25; 2025: 38; 2026: 38.

151 papers.

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  1. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  2. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  3. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 45
  4. 2023MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendationcited by 30
  5. 2021Conversational Fashion Image Retrieval via Multiturn Natural Language Feedbackcited by 29
  6. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  7. 2024LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendationscited by 24
  8. 2021Bootstrapping User and Item Representations for One-Class Collaborative Filteringcited by 22
  9. 2022CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attentioncited by 22
  10. 2024DiffMM: Multi-Modal Diffusion Model for Recommendationcited by 21
  11. 2024AlignRec: Aligning and Training in Multimodal Recommendationscited by 20
  12. 2021Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendationcited by 18
38 more
  1. 2022FedRecAttack: Model Poisoning Attack to Federated Recommendationcited by 18
  2. 2024Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Modelscited by 18
  3. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  4. 2025Gated Multimodal Graph Learning for Personalized Recommendationcited by 15
  5. 2024Multimodality Invariant Learning for Multimedia-Based New Item Recommendationcited by 13
  6. 2021Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoderscited by 12
  7. 2022Collaboration-Aware Graph Convolutional Network for Recommender Systemscited by 12
  8. 2025Multimodal Quantitative Language for Generative Recommendationcited by 12
  9. 2021Privileged Graph Distillation for Cold Start Recommendationcited by 10
  10. 2021SiReN: Sign-Aware Recommendation Using Graph Neural Networkscited by 9
  11. 2021From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systemscited by 9
  12. 2024Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendationscited by 9
  13. 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendationcited by 9
  14. 2025Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systemscited by 9
  15. 2023LD4MRec: Simplifying and Powering Diffusion Model for Multimedia Recommendationcited by 8
  16. 2024GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendationcited by 8
  17. 2024CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systemscited by 8
  18. 2021Debiased Explainable Pairwise Ranking from Implicit Feedbackcited by 7
  19. 2022Supervised Contrastive Learning for Recommendationcited by 7
  20. 2024A Fashion Item Recommendation Model in Hyperbolic Spacecited by 7
  21. 2025A Survey on Large Language Models in Multimodal Recommender Systemscited by 7
  22. 2022Cooperative Retriever and Ranker in Deep Recommenderscited by 6
  23. 2023Recommendation Unlearning via Matrix Correctioncited by 6
  24. 2024Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minimacited by 6
  25. 2024Sequential LLM Framework for Fashion Recommendationcited by 6
  26. 2024Modeling Temporal Positive and Negative Excitation for Sequential Recommendationcited by 6
  27. 2025Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Modelcited by 6
  28. 2025Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendationcited by 6
  29. 2022End-to-End Image-Based Fashion Recommendationcited by 5
  30. 2023Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendationcited by 5
  31. 2023Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendationcited by 5
  32. 2023Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusioncited by 5
  33. 2024Modality-Balanced Learning for Multimedia Recommendationcited by 5
  34. 2025LinkedOut: Linking World Knowledge Representation Out of Video LLM for Next-Generation Video Recommendationcited by 5
  35. 2021TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendationcited by 4
  36. 2021Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industrycited by 4
  37. 2024Positional encoding is not the same as context: A study on positional encoding for sequential recommendationcited by 4
  38. 2024A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendationcited by 4

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