用微调大模型同时预测爆款并生成解释，提升推荐可懂性与准确性。
Following the TRAIL: Predicting and Explaining Tomorrow's Hits with a Fine-Tuned LLM
- 微调LLM联合预测短期爆款趋势与生成自然语言解释。
- 在多个数据集上超越基线模型，且解释内容与趋势信号高度一致。
- 适合需要可解释推荐的场景，如短视频、电商等实时推荐系统。
大型语言模型（LLMs）因其广泛的知识和强大的推理能力，在多个领域得到广泛应用。然而，将其应用于推荐系统面临挑战：难以从大规模稀疏的用户-物品日志中提取用户偏好，且对全量商品库进行实时个性化排序过于耗时。此外，现有推荐系统多关注排名结果，忽略解释生成，而解释能提升预测准确率并增强用户信任。受近期通过预测近期内物品流行度取得优异表现的研究启发，本文提出TRAIL（趋势与解释集成学习器）。TRAIL是一个微调的LLM，能够联合预测短期物品流行度并生成忠实的自然语言解释。它采用对比学习，利用正负样本对使模型的预测分数与解释内容与结构化趋势信号对齐，从而实现准确且可解释的流行度预测。大量实验表明，TRAIL优于多个强基线模型，并生成连贯、有依据的解释。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been widely applied across multiple domains for their broad knowledge and strong reasoning capabilities. However, applying them to recommendation systems is challenging since it is hard for LLMs to extract user preferences from large, sparse user-item logs, and real-time per-user ranking over the full catalog is too time-consuming to be practical. Moreover, many existing recommender systems focus solely on ranking items while overlooking explanations, which could help improve predictive accuracy and make recommendations more convincing to users. Inspired by recent works that achieve strong recommendation performance by forecasting near-term item popularity, we propose TRAIL (TRend and explAnation Integrated Learner). TRAIL is a fine-tuned LLM that jointly predicts short-term item popularity and generates faithful natural-language explanations. It employs contrastive learning with positive and negative pairs to align its scores and explanations with structured trend signals, yielding accurate and explainable popularity predictions. Extensive experiments show that TRAIL outperforms strong baselines and produces coherent, well-grounded explanations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。