# USAR: an Interactive User-specific Aesthetic Ranking Framework for Images

arXiv: https://arxiv.org/abs/1805.01091 · HTML: https://mlsanity.com/paper/1805.01091/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 5 of 5)

- [LAPIS: A novel dataset for personalized image aesthetic assessment](https://mlsanity.com/paper/2504.07670/) (2025)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)
- [Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization](https://mlsanity.com/paper/2407.07176/) (2024)
- [Personalized Image Aesthetic Assessment via Preference-rich Sample Mining and Cohort Merging](https://mlsanity.com/paper/2607.15752/) (2026)
- [Personalizing Text-to-Image Generation to Individual Taste](https://mlsanity.com/paper/2604.07427/) (2026)

## Abstract (arXiv)

When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account. Existing aesthetic models lay emphasis on hand-crafted features or deep features commonly shared by high quality images, but with limited or no consideration for personal preference and user interaction. To that end, we propose a novel and user-friendly aesthetic ranking framework via powerful deep neural network and a small amount of user interaction, which can automatically estimate and rank the aesthetic characteristics of images in accordance with users' preference. Our framework takes as input a series of photos that users prefer, and produces as output a reliable, user-specific aesthetic ranking model matching with users' preference. Considering the subjectivity of personal preference and the uncertainty of user's single selection, a unique and exclusive dataset will be constructed interactively to describe the preference of one individual by retrieving the most similar images with regard to those specified by users. Based on this unique user-specific dataset and sufficient well-designed aesthetic attributes, a customized aesthetic distribution model can be learned, which concatenates both personalized preference and aesthetic rules. We conduct extensive experiments and user studies on two large-scale public datasets, and demonstrate that our framework outperforms those work based on conventional aesthetic assessment or ranking model.