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How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach
by
Li, Min
, Lai, Yuanyuan
, Liu, Junjun
, Liu, Huimin
in
Analysis
/ Business and Management
/ Collaboration
/ Computer Communication Networks
/ Crowdsourcing
/ Data Structures and Information Theory
/ e-Commerce/e-business
/ Embedding
/ IT in Business
/ Matching
/ Methods
/ Operations Research/Decision Theory
/ Problem solving
/ Product development
/ Random walk
/ Semantics
/ Skills
/ Social networks
/ Software development
/ Sparsity
/ Task complexity
/ Teams
2025
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How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach
by
Li, Min
, Lai, Yuanyuan
, Liu, Junjun
, Liu, Huimin
in
Analysis
/ Business and Management
/ Collaboration
/ Computer Communication Networks
/ Crowdsourcing
/ Data Structures and Information Theory
/ e-Commerce/e-business
/ Embedding
/ IT in Business
/ Matching
/ Methods
/ Operations Research/Decision Theory
/ Problem solving
/ Product development
/ Random walk
/ Semantics
/ Skills
/ Social networks
/ Software development
/ Sparsity
/ Task complexity
/ Teams
2025
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Do you wish to request the book?
How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach
by
Li, Min
, Lai, Yuanyuan
, Liu, Junjun
, Liu, Huimin
in
Analysis
/ Business and Management
/ Collaboration
/ Computer Communication Networks
/ Crowdsourcing
/ Data Structures and Information Theory
/ e-Commerce/e-business
/ Embedding
/ IT in Business
/ Matching
/ Methods
/ Operations Research/Decision Theory
/ Problem solving
/ Product development
/ Random walk
/ Semantics
/ Skills
/ Social networks
/ Software development
/ Sparsity
/ Task complexity
/ Teams
2025
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How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach
Journal Article
How to select crowdsourcing teams with limited information? A heterogeneous information network embedding approach
2025
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Overview
Crowdsourcing has become a widely accepted approach to leverage crowds to solve business problems, and how to find proper solvers has been widely discussed. Existing studies mainly focus on matching tasks and individuals for simple tasks. However, the increasing complexity of projects calls for the crowdsourcing team selection, whereas the difficulty lies in the limited background information of team members which leads to data sparsity and cold-start problems. Motivated by this problem, we develop CT-HIN, a heterogeneous information network embedding method to evaluate skill matching and communication by similarity searching based on the pair-wise random walk model from multi-dimension. An empirical evaluation with input data collected from a real-world crowdsourcing platform is conducted to justify our proposed approach.
Publisher
Springer US,Springer,Springer Nature B.V
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