大语言模型增强的时间注意力推荐系统

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关键词：大语言模型；推荐系统；评分预测；时间注意力推荐中图分类号：TP391文献标志码：ADOI：10.7652/xjtuxb202510021 文章编号：0253-987X（2025）10-0221-10

Large Language Model-Augmented Time-Attention Recommender Systems

SUN Haoran'，WANG Xin²，XIONG Fei1 （1. School of Electronic and Information Engineering，Beijing Jiaotong University，Beijing 10oo44，China; 2.Beijing Institute of Computer technology and Application，Beijing 1oo39，China）

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 and Kaggle-Movie. The results demonstrate that LLATR improves root mean square error （RMSE） and mean absolute error （MAE） by 2% 一 5% （204号 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.

Keywords： large language modeling； recommender systems；rating prediction； time-attention recommender

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