mlsanityData to 14 Oct 2026

Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks

arXiv 2406.02979 ↗First posted 5 Jun 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Learning representations of user behavior sequences is crucial for various online services, such as online fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs) have been extensively applied to model sequence relationships, and extract information from similar sequences. While user behavior sequence data volume is usually huge for online applications, directly applying GNN models may lead to substantial computational overhead during both the training and inference stages and make it challenging to meet real-time requirements for online services. In this paper, we leverage graph compression techniques to alleviate the efficiency issue. Specifically, we propose a novel unified framework called ECSeq, to introduce graph compression techniques into relation modeling for user sequence representation learning. The key module of ECSeq is sequence relation modeling, which 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. Empirical experiments on both sequence classification and regression tasks demonstrate the effectiveness of ECSeq. Specifically, with an additional training time of tens of seconds in total on 100,000+ sequences and inference time preserved within 10-4 seconds/sample, ECSeq improves the prediction [email protected] of the widely used LSTM by ~ 5%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 5 Jun 2024.

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32 of 58 references.

References · 32

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20 more
  1. 2021Graph Condensation for Graph Neural Networkscited by 150
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