DCNLLMs: Deep CTR Prediction with LLMs for En-hanced LTL Freight Matching

The logistics sector, particularly less-than-truckload LTL freight, is undergoing rapid development, with recommendation systems becoming increasingly crucial for optimizing operational efficiency. While deep learning and large language models LLMs have revolutionized recommendation systems across various domains, their application in LTL freight matching remains underexplored, with traditional methods still prevalent. To address this gap, this paper introduces DCNLLMs, a novel system designed for predicting click-through rates CTR in LTL cargo-vehicle matching scenarios. DCNLLMs leverages the extensive knowledge base of LLMs to provide expert-level recommendations. A key contribution is a specifically designed fine-tuning framework that aligns CTR prediction with the inherent knowledge of the LLM, significantly enhancing recommendation accuracy and relevance in the LTL logistics context. Comprehensive experiments comparing DCNLLMs with multiple state-of-the-art recommendation models demonstrate the superior effectiveness of our proposed approach. These findings not only validate the efficacy of DCNLLMs but also highlight its transformative potential in innovating LTL freight matching, paving the way for more efficient and intelligent logistics operations.