Large Language Model-Augmented Time-Attention Recommender Systems

SUN Haoran; WANG Xin; XIONG Fei

doi:10.7652/xjtuxb202510021

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Large Language Model-Augmented Time-Attention Recommender Systems

更新时间：2025-09-28

- Large Language Model-Augmented Time-Attention Recommender Systems
- Journal of Xi'an Jiaotong University Vol. 59, Issue 10, Pages: 221-230(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202510021
  CLC： TP391
- Received：30 March 2025，
  
  Published：10 October 2025
- 稿件说明：
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SUN Haoran, WANG Xin, XIONG Fei. Large Language Model-Augmented Time-Attention Recommender Systems[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 221-230.
DOI：

SUN Haoran, WANG Xin, XIONG Fei. Large Language Model-Augmented Time-Attention Recommender Systems[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 221-230. DOI： 10.7652/xjtuxb202510021.

摘要

为了解决传统推荐方法依赖于用户与物品的稀疏交互数据，其难以深入挖掘用户偏好背后的语义逻辑及其随时间演化的动态特征，以及大语言模型（LLM）在推荐系统中的直接应用受限于缺乏结构化交互建模与时间敏感性考虑的问题，提出了一种大语言模型增强的基于时间的推荐模型（LLATR）。LLATR旨在融合语义理解能力与用户兴趣的时间变化建模，以提升推荐精度与系统响应的个性化水平。模型设计了协同特征提取网络和时间特征建模网络，以及结合大语言模型生成的语义评分向量，通过对比学习机制，实现了多模态信息的统一建模，从而构建了具有动态适应能力的推荐框架。结果表明：在MovieLens-100K、Kaggle-Movie这2个数据集上，LLATR的均方根误差、平均绝对误差相对于现有主流模型提升了2%～5%。进一步分析表明，LLM能够补充协同特征之外的深层语义信息，并增强推荐系统对冷启动用户、稀疏数据和复杂行为背景的适应能力，以及有效建模用户的兴趣随时间的非线性演化趋势。

Abstract

To address the limitations of traditional recommendation methods that rely on sparse user-item interaction data—which struggle to deeply mine the semantic logic behind user preferences and their dynamic temporal evolution—as well as the constraints of directly applying large language models (LLMs) in recommendation systems due to their lack of structured interaction modeling and temporal sensitivity

a large language model-augmented time-aware recommendation (LLATR) algorithm is proposed. This algorithm aims to integrate semantic comprehension capabilities with temporal modeling of user interests to enhance recommendation accuracy and system personalization. The method designs a collaborative feature extraction network and a time feature modeling network

and combines the semantic scoring vectors generated by LLMs. Through a contrastive learning mechanism

it achieves unified modeling of multimodal information

thereby constructing a dynamically adaptive recommendation framework. Experiments are conducted on two datasets: MovieLens-100K andKaggle-Movie. The results demonstrate that LLATR improves root mean square error (RMSE) and mean absolute error (MAE) by 2%—5%compared to existing mainstream models. Further analysis reveals that LLMs can supplement deep semantic information beyond collaborative features

enhance the recommendation system's adaptability to cold-start users

sparse data

and complex behavioral contexts

and effectively model the nonlinear evolution trends of user interests over time.

关键词

Keywords

references

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