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International Journal of Crowd Science 2022, 6(4): 167-177 https://doi.org/10.26599/IJCS.2022.9100022
Open Access | Issue | Published: 30 November 2022

Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Show Author's Information Zhishuo Liu1( ),  Yinan Cheng1,  Fang Tian2
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Business Administration Division, Pepperdine University, Malibu, CA 90263, USA
Keywords:
e-commerce, crowd science, crowd intelligence based transaction network, unstructured network, commodity search, search algorithm
Cite this article:
Liu Z, Cheng Y, Tian F. Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network. International Journal of Crowd Science, 2022, 6(4): 167-177. https://doi.org/10.26599/IJCS.2022.9100022
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Abstract Full text About this article

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.


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Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Show Author's information Zhishuo Liu1( ), Yinan Cheng1, Fang Tian2
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Business Administration Division, Pepperdine University, Malibu, CA 90263, USA

Abstract

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.

Keywords: e-commerce, crowd science, crowd intelligence based transaction network, unstructured network, commodity search, search algorithm

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Received: 22 March 2022
Revised: 18 May 2022
Accepted: 24 May 2022
Published: 30 November 2022
Issue date: December 2022

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