TY - JOUR
T1 - Recommender system based on workflow
AU - Zhen, Lu
AU - Huang, George Q.
AU - Jiang, Zuhua
N1 - Funding Information:
George Q. Huang is a Professor at the department of industrial and manufacturing system engineering, the University of Hong Kong. He received the Ph.D. degree in mechanical engineering from Cardiff University, Cardiff, U.K., His main research interests include: collaborative product development, mass customization, supply chain management. He has published extensively in these topics, including over 200 technical papers, half of which have appeared in refereed journals, two monographs and an edited reference book. Prof. Huang received Outstanding Young Researcher Award from The University of Hong Kong (2001) and Overseas Outstanding Young Scholar from Natural Science Foundation of China (2007).
PY - 2009/1
Y1 - 2009/1
N2 - This paper proposes a workflow-based recommender system model on supplying proper knowledge to proper members in collaborative team contexts rather than daily life scenarios, e.g., recommending commodities, films, news, etc. Within collaborative team contexts, more information could be utilized by recommender systems than ordinary daily life contexts. The workflow in collaborative team contains information about relationships among members, roles and tasks, which could be combined with collaborative filtering to obtain members' demands for knowledge. In addition, the work schedule information contained in the workflow could also be employed to determine the proper volume of knowledge that should be recommended to each member. In this paper, we investigate the mechanism of the workflow-based recommender system, and conduct a series of experiments referring to several real-world collaborative teams to validate the effectiveness and efficiency of the proposed methods.
AB - This paper proposes a workflow-based recommender system model on supplying proper knowledge to proper members in collaborative team contexts rather than daily life scenarios, e.g., recommending commodities, films, news, etc. Within collaborative team contexts, more information could be utilized by recommender systems than ordinary daily life contexts. The workflow in collaborative team contains information about relationships among members, roles and tasks, which could be combined with collaborative filtering to obtain members' demands for knowledge. In addition, the work schedule information contained in the workflow could also be employed to determine the proper volume of knowledge that should be recommended to each member. In this paper, we investigate the mechanism of the workflow-based recommender system, and conduct a series of experiments referring to several real-world collaborative teams to validate the effectiveness and efficiency of the proposed methods.
KW - Collaborative filtering
KW - Knowledge management
KW - Recommender system
KW - Workflow
UR - http://www.scopus.com/inward/record.url?scp=70449127075&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2009.08.002
DO - 10.1016/j.dss.2009.08.002
M3 - Journal article
AN - SCOPUS:70449127075
SN - 0167-9236
VL - 48
SP - 237
EP - 245
JO - Decision Support Systems
JF - Decision Support Systems
IS - 1
ER -