Personalized recommendation of crowd-funding campaigns: A bipartite graph approach for sparse data

Wei Wang, Wei Chen, Kevin Zhu, Hongwei Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Bipartite graph is a global optimal algorithm, which enables direct recommendation of crowd funding campaigns. In our method, PersonalRank is applied to calculate global similarity for a network in an iterative manner. It can be applied to recommendations where Cosine similarity function is ineffective. Furthermore, we propose a bipartite graph based collaborative filtering (CF) by combining CF and PersonalRank. The nodes are classified into one of the following two types: user nodes and item nodes. For any two types of nodes, the new model calculates the global similarity between the nodes by PersonalRank, and obtains the recommendation list through CF algorithm. Experiment results show that the bipartite graph based CF achieves better performance for the extremely sparse data from crowd-funding community.

Original languageEnglish (US)
Pages (from-to)1011-1023
Number of pages13
JournalXitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice
Volume37
Issue number4
DOIs
StatePublished - Apr 25 2017
Externally publishedYes

Keywords

  • Bipartite graph
  • Crowd-funding
  • Network structure
  • Recommendation system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Economic Geology
  • Computer Science Applications

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