推荐众筹项目：一种基于图核的极度稀疏隐式反馈链接预测方法。
Recommending Crowdfunding Project: A Graph Kernel-Based Link Prediction Method for Extremely Sparse Implicit Feedback.
机构信息
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
Office of Assets and Laboratory Management, Tongji University, Shanghai 200092, China.
出版信息
Comput Intell Neurosci. 2022 Jul 19;2022:5126140. doi: 10.1155/2022/5126140. eCollection 2022.
It is a critical task to provide recommendation on implicit feedback, and one of the biggest challenges is extreme data sparsity. To tackle the problem, a graph kernel-based link prediction method is proposed in this paper for recommending crowdfunding projects combining graph computing with collaborative filtering. First of all, an investor-project bipartite graph is established based on transaction histories. Then, a random walk graph kernel is constructed and computed, and a one-class SVM classifier is built for link prediction based on implicit feedback. At last, top N recommendations are made according to the ranking of investor-project pairs. Comparative experiments are conducted and the results show that the proposed method achieves the best performance on extremely sparse implicit feedback and outperforms baselines. This paper is of help to improve the success rate of crowdfunding by personalized recommendation and is of significance to enrich the research in recommendation systems.
提供隐式反馈推荐是一项关键任务，最大的挑战之一是数据极度稀疏。为了解决这个问题，本文提出了一种基于图核的链接预测方法，将图计算与协同过滤相结合，用于推荐众筹项目。首先，基于交易历史建立投资者-项目二分图。然后，构建并计算随机游走图核，并基于隐式反馈构建单类 SVM 分类器进行链接预测。最后，根据投资者-项目对的排名进行前 N 名推荐。对比实验结果表明，该方法在极稀疏的隐式反馈下表现最佳，优于基线方法。本文有助于通过个性化推荐提高众筹的成功率，对丰富推荐系统的研究具有重要意义。
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