基于二部图的稀疏数据众筹推荐算法

Q4 Economics, Econometrics and Finance
Hongwei Wang, Shiqin Chen
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引用次数: 5

摘要

稀疏数据处理是推荐人面临的一个普遍问题,尤其是在众筹推荐中。协同过滤(CF)倾向于向用户推荐那些只与相似用户有直接联系的项目,而不能向相似用户推荐有间接联系的项目。因此,CF在Kickstarter等稀疏数据的情况下表现不佳。提出了一种基于二部图的间接众筹活动推荐方法。PersonalRank适用于计算全局相似度;与局部相似度相反,对于网络的任何节点,我们以迭代的方式使用PersonalRank生成CF无效的推荐列表。在此基础上,我们将CF与PersonalRank相结合,提出了一个基于二部图的CF模型。新模型将节点分为以下两种类型:用户节点和活动节点。对于任意两种类型的节点,它们之间的全局相似度由PersonalRank计算。最后,通过CF算法对任意节点生成推荐列表。实验结果表明,基于二部图的CF在众筹活动的极稀疏数据推荐中取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data
It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns.
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来源期刊
Banking and Finance Review
Banking and Finance Review Economics, Econometrics and Finance-Finance
CiteScore
0.30
自引率
0.00%
发文量
1
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