论文研究-众筹项目的个性化推荐：面向稀疏数据的二分图模型.pdf
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37
4
Vol.37, No.4
2017
4
Systems Engineering — Theory & Practice Apr., 2017
doi: 10.12011/1000-6788(2017)04-1011-13
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Personalized recommendation of crowd-funding campaigns:
A bipartite graph approach for sparse data
WANG Wei
1
,CHENWei
2
,ZHUKevin
3
, WANG Hongwei
4
(1. College of Business Administration, Huaqiao University, Quanzhou 362021, China; 2. Eller College of Management,
University of Arizona, Tucson 85721, USA; 3. Rady School of Management, University of California, San Diego 92093, USA;
4. School of Economics and Management, Tongji University, Shanghai 200092, China)
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 Per-
sonalRank. 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.
Keywords crowd-funding; recommendation system; bipartite graph; network structure
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Foundation item: Huaqiao University’s Academic Project Supported by the Fundamental Research Funds for the Central
Universities (16SKGC-QG14); National Natural Science Foundation of China (71601082, 71371144); Social Science Planning
Project of Fujian Province, China (FJ2016B075)
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: Wang W, Chen W, Zhu K, et al. Personalized recommendation of crowd-funding campaigns: A bipartite graph
approach for sparse data[J]. Systems Engineering — Theory & Practice, 2017, 37(4): 1011–1023.
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