# Attribute-aware Explainable Complementary Clothing Recommendation

arXiv: https://arxiv.org/abs/2107.01655 · HTML: https://mlsanity.com/paper/2107.01655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [Learning Type-Aware Embeddings for Fashion Compatibility](https://mlsanity.com/paper/1803.09196/) (2018)
- [Progressive Graph Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2006.12087/) (2020)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2105.03300/) (2021)
- [Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences](https://mlsanity.com/paper/1509.07473/) (2015)
- [Adversarial Bipartite Graph Learning for Video Domain Adaptation](https://mlsanity.com/paper/2007.15829/) (2020)
- [Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches](https://mlsanity.com/paper/2007.13314/) (2020)
- [Attribute-Aware Attention Model for Fine-grained Representation Learning](https://mlsanity.com/paper/1901.00392/) (2019)
- [Complete the Look: Scene-based Complementary Product Recommendation](https://mlsanity.com/paper/1812.01748/) (2018)
- [Sequence-Aware Factorization Machines for Temporal Predictive Analytics](https://mlsanity.com/paper/1911.02752/) (2019)
- [Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data](https://mlsanity.com/paper/1608.03016/) (2016)
- [Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation](https://mlsanity.com/paper/1806.08977/) (2018)
- [Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation](https://mlsanity.com/paper/1908.00169/) (2019)
- [Recommending Outfits from Personal Closet](https://mlsanity.com/paper/1804.09979/) (2018)
- [Context-Aware Attention-Based Data Augmentation for POI Recommendation](https://mlsanity.com/paper/2106.15984/) (2021)
- [Collaborative Learning for Extremely Low Bit Asymmetric Hashing](https://mlsanity.com/paper/1809.09329/) (2018)
- [TransNFCM: Translation-Based Neural Fashion Compatibility Modeling](https://mlsanity.com/paper/1812.10021/) (2018)
- [SADIH: Semantic-Aware DIscrete Hashing](https://mlsanity.com/paper/1904.01739/) (2019)
- [Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation](https://mlsanity.com/paper/1503.03650/) (2015)
- [Deep Collaborative Discrete Hashing with Semantic-Invariant Structure](https://mlsanity.com/paper/1911.01565/) (2019)

## Built on by (most cited first, 2 of 2)

- [Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation](https://mlsanity.com/paper/2202.06466/) (2022)
- [Complementary Recommendation in E-commerce: Definition, Approaches, and Future Directions](https://mlsanity.com/paper/2403.16135/) (2024)

## Abstract (arXiv)

Modelling mix-and-match relationships among fashion items has become increasingly demanding yet challenging for modern E-commerce recommender systems. When performing clothes matching, most existing approaches leverage the latent visual features extracted from fashion item images for compatibility modelling, which lacks explainability of generated matching results and can hardly convince users of the recommendations. Though recent methods start to incorporate pre-defined attribute information (e.g., colour, style, length, etc.) for learning item representations and improving the model interpretability, their utilisation of attribute information is still mainly reserved for enhancing the learned item representations and generating explanations via post-processing. As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process. This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec). Specifically, AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item's visual feature. The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes. Extensive experiments have demonstrated that, by making full use of the explicit attributes in the recommendation process, AFRec is able to achieve state-of-the-art recommendation accuracy and generate intuitive explanations at the same time.