Summary of A Click-through Rate Prediction Method Based on Cross-importance Of Multi-order Features, by Hao Wang and Nao Li
A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features
by Hao Wang, Nao Li
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 FiiNet model is a novel approach to click-through rate prediction (CTR) that explicitly constructs multi-order feature crosses using the selective kernel network (SKNet). Unlike existing models, FiiNet dynamically learns the importance of feature interaction combinations in a fine-grained manner, increasing attention weights for important cross combinations and reducing weights for featureless crosses. This enables improved recommendation performance and interpretability. The model is evaluated on two real datasets, outperforming other CTR prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FiiNet model is a new way to predict what people will click on when they see an online ad. It works by combining different features of the ad in important ways, which helps it make better predictions. The model can learn what combinations are most important and use that information to give recommendations. This means that advertisers can get more accurate ideas about who is likely to be interested in their ads. |
Keywords
» Artificial intelligence » Attention
