Summary of Neural Graph Matching For Video Retrieval in Large-scale Video-driven E-commerce, by Houye Ji et al.
Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce
by Houye Ji, Ye Tang, Zhaoxin Chen, Lixi Deng, Jun Hu, Lei Su
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a novel approach to the video retrieval task in video-driven e-commerce, which leverages the dual graph to model user-video and user-item interactions. The authors develop a bi-level Graph Matching Network (GMN) that consists of node- and preference-level graph matching to generate and improve user embeddings. Experimental results show significant improvements over state-of-the-art approaches on a well-known video-driven e-commerce platform, with an AUC increase of 1.9% and CTR improvement of 7.15%. The GMN is applied to the task of recommending videos and items that match users’ preferences, showing potential in promoting sales and stimulating consumer confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way for video-driven e-commerce platforms to show users products that fit their preferences. They use graphs to connect people, videos, and items, and create a special algorithm called the Graph Matching Network (GMN). This GMN helps find the best matches by comparing nodes and preferences. The results are impressive, with a big increase in accuracy and a significant boost in customer engagement. |
Keywords
* Artificial intelligence * Auc
