Summary of Efficient User Sequence Learning For Online Services Via Compressed Graph Neural Networks, by Yucheng Wu et al.
Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks
by Yucheng Wu, Liyue Chen, Yu Cheng, Shuai Chen, Jinyu Xu, Leye Wang
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 the challenge of learning representations for user behavior sequences in online services, such as fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs) are typically used to model sequence relationships, but their computational overhead can be significant during training and inference. To alleviate this issue, the authors propose a novel framework called ECSeq, which incorporates graph compression techniques into relation modeling for user sequence representation learning. The key module of ECSeq explores relationships among sequences to enhance sequence representation learning and employs graph compression algorithms to achieve high efficiency and scalability. ECSeq also exhibits plug-and-play characteristics, seamlessly augmenting pre-trained sequence representation models without modifications. Experimental results demonstrate the effectiveness of ECSeq, improving the prediction accuracy of LSTM by around 5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how online services can learn from user behavior patterns. It focuses on a specific problem – processing large amounts of data efficiently – and provides a new way to do it called ECSeq. The authors show that ECSeq is faster and more accurate than other methods, like LSTM. This means that ECSeq could be used in real-time applications, like detecting suspicious transactions online. |
Keywords
» Artificial intelligence » Inference » Lstm » Representation learning
