Recommender system based on workflow

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 real-world collaborative teams to validate the effectiveness and efficiency of the proposed methods. © 2009 Elsevier B.V. All rights reserved.