TY  - CONF
T1  - BEHAVIOR THEORY ENABLED GENDER CLASSIFICATION METHOD
T2  - the Fifteeth workshop on e-Business (WeB 2016) in Dublin
Y1  - 2017
A1  - Wang,Jing
A1  - Yan,Xiangbin
A1  - Zhu,Bin
KW  - BIS
KW  - Business Analytics
JA  - the Fifteeth workshop on e-Business (WeB 2016) in Dublin
U2  - b
U4  - 145142386688
ID  - 145142386688
ER  - 

TY  - JOUR
T1  - The Hl-index: Improvement of H-index Based on Quality of Citing Papers
JF  - Akadémiai Kiadó and Springer Science+Business Media
Y1  - 2014
A1  - Zai,Li
A1  - Yan,Xiangbin
A1  - Zhu,Bin
KW  - BIS
KW  - Business Analytics
AB  - This paper proposes hl-index as an improvement of the h-index, a popular measurement for the research quality of academic researchers. Although the h-index integrates the number of publications and the academic impact of each publication to evaluate the productivity of a researcher, it assumes that all papers that cite an academic article contribute equally to the academic impact of this article. This assumption, of course, could not be true in most times. The citation from a well-cited paper certainly brings more attention to the article than the citation from a paper that people do not pay attention to. It therefore becomes important to integrate the impact of papers that cite a researcher’s work into the evaluation of the productivity of the researcher. Constructing a citation network among academic papers, this paper therefore proposes hl-index that integrating the h-index with the concept of lobby index, a measures that has been used to evaluate the impact of a node in a complex network based on the impact of other nodes that the focal node has direct link with. This paper also explores the characteristics of the proposed hl-index by comparing it with citations, h-index and its variant g-index.
VL  - 98
CP  - 2
U2  - a
U4  - 69565954048
ID  - 69565954048
ER  - 

TY  - ABST
T1  - Gender Classification for Product Reviewers in China: A Data-Driven Approach
Y1  - 2013
A1  - Zhu,Bin
A1  - Yan,Xiangbin
A1  - Wang,Jing
KW  - BIS
KW  - Business Analytics
AB  - While it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in those studies and gender type usually varies with context. The prototype system we propose to demo validates a framework for the development of gender classification that uses a more “data-driven” approach. It constantly extracts content-specific features from the discussion content. And the system could automatically adjust itself to accommodate the contextual changes in order to achieve better classification accuracy.
UR  - http://www.som.buffalo.edu/isinterface/wits2013/
U2  - d
U4  - 88335765504
ID  - 88335765504
ER  -