Summary of A Unified Graph Transformer For Overcoming Isolations in Multi-modal Recommendation, by Zixuan Yi et al.
A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
by Zixuan Yi, Iadh Ounis
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to personalized recommendation systems for e-commerce platforms. The authors argue that existing methods use isolated processes for feature extraction and modality modeling, which can harm recommendation performance. They hypothesize that a unified model addressing both processes will enable the consistent extraction and fusion of joint multi-modal features. The proposed Unified Multi-modal Graph Transformer (UGT) model combines a multi-way transformer for extracting aligned features from raw data with a graph neural network for jointly fusing user/item representations and multi-modal features. The authors demonstrate significant effectiveness gains when using their UGT model, optimized with multi-modal recommendation losses. This paper contributes to the development of more effective personalized recommendation systems for online platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re shopping online and you want recommendations based on what you like. Right now, most recommendation systems don’t work well because they look at different types of information (like images or text) separately. This paper proposes a new way to combine this information to make better recommendations. The authors think that by using one model to process all the information together, we can get more accurate results. They test their idea and find that it works really well, especially when combined with special losses used in recommendation systems. |
Keywords
» Artificial intelligence » Feature extraction » Graph neural network » Multi modal » Transformer
