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The recommendation of appropriate development pathways, also known asecological civilization patterns for achieving Sustainable Development Goals(namely, sustainable development patterns), are of utmost importance forpromoting ecological, economic, social, and resource sustainability in aspecific region. To achieve this, the recommendation process must carefullyconsider the region's natural, environmental, resource, and economiccharacteristics. However, current recommendation algorithms in the field ofcomputer science fall short in adequately addressing the spatial heterogeneityrelated to environment and sparsity of regional historical interaction data,which limits their effectiveness in recommending sustainable developmentpatterns. To overcome these challenges, this paper proposes a method calledUser Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize thehigh-density linking capability of the pruned User Graph to address the issueof spatial heterogeneity neglect in recommendation algorithms. Secondly, weconstruct an Intent Graph by incorporating the intent network, which capturesthe preferences for attributes including environmental elements of targetregions. This approach effectively alleviates the problem of sparse historicalinteraction data in the region. Through extensive experiments, we demonstratethat UGPIG outperforms state-of-the-art recommendation algorithms like KGCN,KGAT, and KGIN in sustainable development pattern recommendations, with amaximum improvement of 9.61% in Top-3 recommendation performance.

Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations