# Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach

arXiv: https://arxiv.org/abs/1905.12862 · HTML: https://mlsanity.com/paper/1905.12862/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 17 of 17)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [VIP5: Towards Multimodal Foundation Models for Recommendation](https://mlsanity.com/paper/2305.14302/) (2023)
- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback](https://mlsanity.com/paper/2106.04128/) (2021)
- [CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention](https://mlsanity.com/paper/2204.06519/) (2022)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [A Survey on Large Language Models in Multimodal Recommender Systems](https://mlsanity.com/paper/2505.09777/) (2025)
- [Sequential LLM Framework for Fashion Recommendation](https://mlsanity.com/paper/2410.11327/) (2024)
- [End-to-End Image-Based Fashion Recommendation](https://mlsanity.com/paper/2205.02923/) (2022)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Knowledge-grounded Natural Language Recommendation Explanation](https://mlsanity.com/paper/2308.15813/) (2023)
- [Positional encoding is not the same as context: A study on positional encoding for sequential recommendation](https://mlsanity.com/paper/2405.10436/) (2024)
- [ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation](https://mlsanity.com/paper/2606.28357/) (2026)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation](https://mlsanity.com/paper/2108.08022/) (2021)
- [Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization](https://mlsanity.com/paper/2207.07278/) (2022)
- [Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment](https://mlsanity.com/paper/2501.03085/) (2025)

## Abstract (arXiv)

In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.