  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.
