The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development patterns), are of utmost importance for promoting ecological, economic, social, and resource sustainability in a specific region. To achieve this, the recommendation process must carefully consider the region's natural, environmental, resource, and economic characteristics. However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns. To overcome these challenges, this paper proposes a method called User Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize the high-density linking capability of the pruned User Graph to address the issue of spatial heterogeneity neglect in recommendation algorithms. Secondly, we construct an Intent Graph by incorporating the intent network, which captures the preferences for attributes including environmental elements of target regions. This approach effectively alleviates the problem of sparse historical interaction data in the region. Through extensive experiments, we demonstrate that UGPIG outperforms state-of-the-art recommendation algorithms like KGCN, KGAT, and KGIN in sustainable development pattern recommendations, with a maximum improvement of 9.61% in Top-3 recommendation performance.

翻译：可持续发展路径（即实现可持续发展目标的生态文明模式）的推荐对于促进特定区域的生态、经济、社会及资源可持续性至关重要。为此，推荐过程必须充分考虑区域的自然、环境、资源及经济特征。然而，当前计算机科学领域的推荐算法在应对环境相关空间异质性及区域历史交互数据稀疏性方面存在不足，这限制了其在推荐可持续发展模式中的有效性。为克服上述挑战，本文提出一种名为“剪枝-意图图”（User Graph after Pruning and Intent Graph, 简称UGPIG）的方法。首先，利用剪枝用户图的高密度连接能力解决推荐算法对空间异质性忽视的问题；其次，通过引入意图网络构建意图图，捕捉目标区域的环境要素等属性偏好，有效缓解区域历史交互数据稀疏性问题。大量实验表明，UGPIG在可持续发展模式推荐中优于KGCN、KGAT及KGIN等主流算法，Top-3推荐性能最高提升9.61%。