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Recommending Crowdfunding Project: A Graph Kernel-Based Link Prediction Method for Extremely Sparse Implicit

Pei Yin1, Ya Chen1, Huan Wang1

  • 1Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.

Computational Intelligence and Neuroscience
|August 1, 2022
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Summary
This summary is machine-generated.

This study introduces a graph kernel method for crowdfunding recommendations, effectively addressing extreme data sparsity in implicit feedback. The approach enhances personalized recommendations, improving crowdfunding success rates.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Implicit feedback recommendation systems face significant challenges due to extreme data sparsity.
  • Crowdfunding platforms require effective recommendation systems to connect investors with suitable projects.

Purpose of the Study:

  • To propose a novel graph kernel-based link prediction method for recommending crowdfunding projects.
  • To address the challenge of extreme data sparsity in implicit feedback datasets.

Main Methods:

  • Constructing an investor-project bipartite graph from transaction histories.
  • Developing a random walk graph kernel and a one-class SVM classifier for link prediction.
  • Generating top N recommendations based on ranked investor-project pairs.

Main Results:

  • The proposed method demonstrates superior performance on extremely sparse implicit feedback data.
  • Comparative experiments show the method outperforms existing baseline approaches.
  • The technique effectively handles data sparsity in recommendation tasks.

Conclusions:

  • The graph kernel-based link prediction method is effective for personalized crowdfunding recommendations.
  • This research contributes to improving crowdfunding success rates through enhanced recommendation systems.
  • The study enriches the field of recommendation systems, particularly for sparse implicit feedback scenarios.