The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical context. There are two reasons for this. Firstly, regions have spatial heterogeneity, and the (ECPRS)needs to consider factors like climate, topography, vegetation, etc., to recommend civilization patterns adapted to specific ecological environments, ensuring the feasibility and practicality of the recommendations. Secondly, the abstract features of the ecological civilization patterns in the real world have not been fully utilized., resulting in poor richness in their embedding representations and consequently, lower performance of the recommendation system. Considering these limitations, we propose the ECPR-MML method. Initially, based on the novel method UGPIG, we construct a knowledge graph to extract regional representations incorporating spatial heterogeneity features. Following that, inspired by the significant progress made by Large Language Models (LLMs) in the field of Natural Language Processing (NLP), we employ Large LLMs to generate multimodal features for ecological civilization patterns in the form of text and images. We extract and integrate these multimodal features to obtain semantically rich representations of ecological civilization. Through extensive experiments, we validate the performance of our ECPR-MML model. Our results show that F1@5 is 2.11% higher compared to state-of-the-art models, 2.02% higher than NGCF, and 1.16% higher than UGPIG. Furthermore, multimodal data can indeed enhance recommendation performance. However, the data generated by LLM is not as effective as real data to a certain extent.

翻译：生态文明模式推荐系统（ECPRS）旨在为目标区域推荐适宜的生态文明模式，以促进可持续发展并缩小区域差异。然而，当前代表性推荐方法并不适用于地理语境下的生态文明模式推荐。其原因有二：其一，区域存在空间异质性，ECPRS需考虑气候、地形、植被等因素，从而推荐适应特定生态环境的文明模式，确保推荐的可行性与实用性；其二，现实世界中生态文明模式的抽象特征尚未得到充分利用，导致其嵌入表征的丰富性不足，进而降低了推荐系统的性能。针对上述局限，我们提出ECPR-MML方法。首先，基于新型UGPIG方法构建知识图谱，提取融合空间异质性特征的区域表征。其次，受大语言模型（LLMs）在自然语言处理（NLP）领域取得显著进展的启发，我们采用LLMs以文本和图像形式生成生态文明模式的多模态特征，通过提取与融合这些多模态特征，获得语义丰富的生态文明表征。通过大量实验验证了ECPR-MML模型的性能。结果表明，与最先进模型相比，F1@5指标提升2.11%，相比NGCF提升2.02%，相比UGPIG提升1.16%。此外，多模态数据确实能够增强推荐性能，但LLM生成的数据在某种程度上仍不及真实数据的有效性。