# Comparative Deep Learning of Hybrid Representations for Image Recommendations

arXiv: https://arxiv.org/abs/1604.01252 · HTML: https://mlsanity.com/paper/1604.01252/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 6 of 6)

- [StyleGAN-Human: A Data-Centric Odyssey of Human Generation](https://mlsanity.com/paper/2204.11823/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer](https://mlsanity.com/paper/2307.07754/) (2023)
- [Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion](https://mlsanity.com/paper/2312.15826/) (2023)
- [An Evaluation Framework for Product Images Background Inpainting based on Human Feedback and Product Consistency](https://mlsanity.com/paper/2412.17504/) (2024)
- [Photos Are All You Need for Reciprocal Recommendation in Online Dating](https://mlsanity.com/paper/2108.11714/) (2021)

## Abstract (arXiv)

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other state-of-the-art image recommendation solutions.