Data to 7 Oct 2026

Image-based Recommendations on Styles and Substitutes

arXiv 1506.04757 ↗First posted 15 Jun 2015cs.CV cs.IRcited by 702

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Abstract (arXiv)

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

Key facts. Cited by 702 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2015.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 96; 2022: 103; 2023: 121; 2024: 137; 2025: 147; 2026: 97.

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2019Fine-Tuning Language Models from Human Preferencescited by 2,124
2021DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Expertscited by 434
2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
2023Data Selection for Language Models via Importance Resamplingcited by 322
2021GRAND: Graph Neural Diffusioncited by 258
2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135

38 more

2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
2021LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5cited by 103
2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
2022Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLPcited by 99
2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
2022Intent Contrastive Learning for Sequential Recommendationcited by 90
2024Deep Multimodal Learning with Missing Modality: A Surveycited by 89
2023On Generative Agents in Recommendationcited by 88
2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
2021Graph Barlow Twins: A self-supervised representation learning framework for graphscited by 85
2021Beltrami Flow and Neural Diffusion on Graphscited by 84
2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 84
2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
2021Mining Latent Structures for Multimedia Recommendationcited by 79
2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
2021Backdoor Pre-trained Models Can Transfer to Allcited by 79
2023Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPTcited by 78
2023In-Context Learning with Iterative Demonstration Selectioncited by 74
2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
2022ELLE: Efficient Lifelong Pre-training for Emerging Datacited by 70
2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 67
2021Lorentzian Graph Convolutional Networkscited by 66
2024HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modelingcited by 63
2022Understanding and Improving Graph Injection Attack by Promoting Unnoticeabilitycited by 62
2025SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptationcited by 61
2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
2021Topology-Imbalance Learning for Semi-Supervised Node Classificationcited by 56
2022Personalized Subgraph Federated Learningcited by 54
2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 53
2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49

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