Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph
  Pruning and Intent Graph for Effective Recommendations
Zhihang Yu Shu Wang Yunqiang Zhu Wen Yuan Xiaoliang Dai Zhiqiang Zou
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.