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The Ecological Civilization Pattern Recommendation System (ECPRS) aims torecommend suitable ecological civilization patterns for target regions,promoting sustainable development and reducing regional disparities. However,the current representative recommendation methods are not suitable forrecommending ecological civilization patterns in a geographical context. Thereare two reasons for this. Firstly, regions have spatial heterogeneity, and the(ECPRS)needs to consider factors like climate, topography, vegetation, etc., torecommend civilization patterns adapted to specific ecological environments,ensuring the feasibility and practicality of the recommendations. Secondly, theabstract features of the ecological civilization patterns in the real worldhave not been fully utilized., resulting in poor richness in their embeddingrepresentations and consequently, lower performance of the recommendationsystem. Considering these limitations, we propose the ECPR-MML method.Initially, based on the novel method UGPIG, we construct a knowledge graph toextract regional representations incorporating spatial heterogeneity features.Following that, inspired by the significant progress made by Large LanguageModels (LLMs) in the field of Natural Language Processing (NLP), we employLarge LLMs to generate multimodal features for ecological civilization patternsin the form of text and images. We extract and integrate these multimodalfeatures to obtain semantically rich representations of ecologicalcivilization. Through extensive experiments, we validate the performance of ourECPR-MML model. Our results show that F1@5 is 2.11% higher compared tostate-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 acertain extent.

A Multimodal Ecological Civilization Pattern Recommendation Method Based on Large Language Models and Knowledge Graph