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Article summary:
1. The article proposes a novel module called Dual Graph enhanced Embedding to address the feature sparsity and behavior sparsity problems in CTR prediction.
2. The proposed Dual Graph enhanced Embedding Neural Network (DG-ENN) for CTR prediction significantly outperforms state-of-the-art models.
3. The framework will be open-sourced based on MindSpore in the near future.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims through comprehensive experiments on three real-world industrial datasets. The article does not appear to have any biases or one-sided reporting, as it presents both sides of the argument equally and objectively. Furthermore, there are no unsupported claims or missing points of consideration, as all claims are backed up by evidence from the experiments conducted. Additionally, there are no unexplored counterarguments or promotional content present in the article, as it focuses solely on presenting the results of the experiments conducted and does not attempt to promote any particular product or service. Finally, possible risks are noted in the article, as it mentions that further case studies need to be conducted to prove that Dual graph enhanced embedding can alleviate feature sparsity and behavior sparsity problems. In conclusion, this article is trustworthy and reliable due to its objective presentation of both sides of the argument and its use of evidence from experiments to back up its claims.