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.

翻译：大语言模型（LLMs）凭借其广泛的知识和强大的推理能力，已在多个领域得到广泛应用。然而，将其应用于推荐系统仍面临挑战：一方面，LLMs难以从大规模、稀疏的用户-物品交互日志中有效提取用户偏好；另一方面，在全量物品库上对每个用户进行实时排序的计算开销过大，难以实际部署。此外，现有推荐系统大多仅关注物品排序，而忽视了可提升预测准确性、增强用户信任度的解释生成。受近期通过预测短期物品流行度实现优异推荐性能的研究启发，本文提出TRAIL（趋势与解释集成学习器）。TRAIL是一种经过微调的大语言模型，能够联合预测短期物品流行度并生成忠实于数据的自然语言解释。该模型采用包含正负样本对的对比学习方法，使其评分和解释与结构化趋势信号对齐，从而产生准确且可解释的流行度预测。大量实验表明，TRAIL在性能上优于现有强基线模型，并能生成连贯、依据充分的解释。