Summary of Multi-intent Aware Contrastive Learning For Sequential Recommendation, by Junshu Huang et al.
Multi-intent Aware Contrastive Learning for Sequential Recommendation
by Junshu Huang, Zi Long, Xianghua Fu, Yin Chen
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 addresses a crucial aspect of sequence recommendation (SR) models: incorporating intent into the training process. Current models, relying on contrastive learning, assume single-intent representations for user-item interaction sequences. However, this oversimplification neglects the complexity of real-world scenarios where multiple intents are present. The authors argue that SR models should account for multi-intent information to better reflect these diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make online recommendations more accurate. Right now, some methods try to predict what people want by looking at their past behavior and assuming they have one main reason for doing something. But in real life, people often do things for many different reasons. The authors think that recommendation systems should be able to handle these multiple reasons to give better suggestions. |
