Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns due to their innate weakness in interpreting numerical features and the overhead for long context, where the temporal relations among user behaviors, subtle quantitative signals among different ratings, and various side features of items are not well explored. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it, leaving these problems unsolved. In this paper, we propose ELCoRec to Enhance Language understanding with CoPropagation of numerical and categorical features for Recommendation. Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items. The parallel propagation mechanism could stabilize heterogeneous features and offer an informative user preference encoding, which is then injected into the language models via soft prompting at the cost of a single token embedding. To further obtain the user's recent interests, we proposed a novel Recent interaction Augmented Prompt (RAP) template. Experiment results over three datasets against strong baselines validate the effectiveness of ELCoRec. The code is available at https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md.

翻译：大型语言模型在自然语言处理领域蓬勃发展，其在推荐系统中的潜力已受到广泛关注。尽管面向推荐的微调模型展现出智能特性，但由于其固有缺陷——难以有效解析数值特征，以及处理长上下文时存在开销，大型语言模型仍难以完全理解用户行为模式。具体而言，用户行为间的时间关联、不同评分间的细微量化信号以及物品的多样化辅助特征均未得到充分探索。现有研究仅基于给定文本数据对单一大型语言模型进行微调，未引入上述关键信息，导致这些问题悬而未决。本文提出ELCoRec模型，通过数值与类别特征的协同传播来增强语言理解能力，进而提升推荐效果。具体而言，我们设计了一种基于图注意力网络的专家模型，通过并行传播历史物品的时间关联、评分信号及多样化辅助信息，从而更有效地编码用户偏好，并将此能力注入大型语言模型。该并行传播机制能够稳定异构特征并生成信息丰富的用户偏好编码，随后仅需通过单个词元嵌入的软提示方式即可将其注入语言模型。为进一步捕捉用户近期兴趣，我们提出了一种新颖的近期交互增强提示模板。在三个数据集上对比多个强基线的实验结果表明了ELCoRec的有效性。代码已发布于https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md。