Summary of Path-based Summary Explanations For Graph Recommenders (extended Version), by Danae Pla Karidi and Evaggelia Pitoura
Path-based summary explanations for graph recommenders (extended version)
by Danae Pla Karidi, Evaggelia Pitoura
First submitted to arxiv on: 29 Oct 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 introduces summary explanations for graph-based recommendation models, providing insights into the collective behavior of the recommender. Traditional path-based explanations focus on individual recommendations, whereas summary explanations highlight why users or groups receive item sets and why items are recommended to specific users. The authors propose a novel method using efficient graph algorithms, such as Steiner Tree and Prize-Collecting Steiner Tree, to summarize explanations while preserving essential information. This approach reduces complexity and size, making explanations more comprehensible for users and useful for model developers. Evaluations across multiple metrics demonstrate that the proposed summaries outperform baseline methods in various quality aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand why a group of people gets certain recommendations or why an item is recommended to them. It’s like trying to figure out why a music streaming service suggests some songs over others. The authors came up with a new way to explain these recommendations by looking at the relationships between users and items on a graph. Their method makes it easier for people to understand why they got certain recommendations, while also helping developers of recommendation systems make better suggestions. |
