Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems

• URL: http://arxiv.org/abs/2507.01035v1
• Date: Sat, 21 Jun 2025 03:10:50 GMT
• Title: Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems
• Authors: Yushang Zhao, Haotian Lyu, Yike Peng, Aijia Sun, Feng Jiang, Xinyue Han,
• Abstract summary: This study considers computational bottlenecks involved in hybrid Graph Neural Network (GNN) and Large Language Model (LLM)-based recommender systems (ReS)<n>It recommends the use of FPGA as well as LoRA for real-time deployment.
• Score: 4.633338944734091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The incessant advent of online services demands high speed and efficient recommender systems (ReS) that can maintain real-time performance along with processing very complex user-item interactions. The present study, therefore, considers computational bottlenecks involved in hybrid Graph Neural Network (GNN) and Large Language Model (LLM)-based ReS with the aim optimizing their inference latency and training efficiency. An extensive methodology was used: hybrid GNN-LLM integrated architecture-optimization strategies(quantization, LoRA, distillation)-hardware acceleration (FPGA, DeepSpeed)-all under R 4.4.2. Experimental improvements were significant, with the optimal Hybrid + FPGA + DeepSpeed configuration reaching 13.6% more accuracy (NDCG@10: 0.75) at 40-60ms of latency, while LoRA brought down training time by 66% (3.8 hours) in comparison to the non-optimized baseline. Irrespective of domain, such as accuracy or efficiency, it can be established that hardware-software co-design and parameter-efficient tuning permit hybrid models to outperform GNN or LLM approaches implemented independently. It recommends the use of FPGA as well as LoRA for real-time deployment. Future work should involve federated learning along with advanced fusion architectures for better scalability and privacy preservation. Thus, this research marks the fundamental groundwork concerning next-generation ReS balancing low-latency response with cutting-edge personalization.

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