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

arXiv 2309.11741 ↗First posted 21 Sep 2023cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 17

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5 more
  1. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  2. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  3. 2018EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphscited by 20
  4. 2021GLocal-K: Global and Local Kernels for Recommender Systemscited by 11
  5. 2022Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Qualitycited by 5

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