Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
553872 | Decision Support Systems | 2009 | 9 Pages |
Abstract
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 realworld collaborative teams to validate the effectiveness and efficiency of the proposed methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Lu Zhen, George Q. Huang, Zuhua Jiang,