Summary of When Factorization Meets Argumentation: Towards Argumentative Explanations, by Jinfeng Zhong et al.
When factorization meets argumentation: towards argumentative explanations
by Jinfeng Zhong, Elsa Negre
First submitted to arxiv on: 13 May 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 The proposed model combines factorization-based methods with argumentation frameworks (AFs) to provide clear explanations for user-item interactions. By treating feature attribution as a structured argumentation procedure, the model enhances its interpretability and enables the generation of easily understandable recommendations. The integration of AFs also allows the model to seamlessly incorporate side information, such as user contexts, leading to more accurate predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make sense of why you like certain things. They combine two ideas: factorization-based models (like Netflix’s) and argumentation frameworks (which help explain things). This lets the model provide clear explanations for why it recommends something, making it easier to understand. It also incorporates extra information, like what time of day you’re watching TV, to make recommendations more accurate. |
