[2603.21508] Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences

Computer Science > Machine Learning arXiv:2603.21508 (cs) [Submitted on 23 Mar 2026] Title:Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences Authors:Chen Gong, Zhenzhe Zheng, Yiliu Chen, Sheng Wang, Fan Wu, Guihai Chen View a PDF of the paper titled Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences, by Chen Gong and 5 other authors View PDF HTML (experimental) Abstract:Machine learning models are widely integrated into modern mobile apps to analyze user behaviors and deliver personalized services. Ensuring low-latency on-device model execution is critical for maintaining high-quality user experiences. While prior research has primarily focused on accelerating model inference with given input features, we identify an overlooked bottleneck in real-world on-device model execution pipelines: extracting input features from raw application logs. In this work, we explore a new direction of feature extraction optimization by analyzing and eliminating redundant extraction operations across different model features and consecutive model inferences. We then introduce AutoFeature, an automated feature extraction engine designed to accelerate on-device feature extraction process without compromising model inference accuracy. AutoFeature comprises three core designs: (1) graph abstraction to formulate the extraction workflows of different input features as one directed acyclic graph, (2) graph optimization to ...