Knowledge Recommender Systems

This book proposes a novel mode of knowledge management tools -- knowledge recommender systems (KRS), which can recommend proper knowledge to proper users in proper context. Nowadays, knowledge query systems are the most common mode of knowledge management. However, when users undertake some knowledge-intensive tasks, e.g., product innovative design, the users often do not know which keywords should be keyed in for searching their wanted knowledge through the conventional query systems. Moreover, by using the query systems, the obtained results are usually within the predefined scopes or constrained by traditional design thinking; they hardly provide the users with any sparks of inspiration for innovation. Therefore, the KRSs can act as a supplementary way for the conventional knowledge query systems. Motivated by the above objectives, the author of this book has dedicated several years in efforts to study the technical issues of designing and implementing the KRSs. This book elaborates on five designs of the KRSs, e.g., workflow based KRSs, information flow based KRSs, multi-dimensional KRSs, distributed KRSs, and innovation-oriented KRSs.

Autorentext

Dr. Zhen is an Associate Professor at Department of Management Science and Engineering, School of Management, Shanghai University, Shanghai, China. He received the B.E. and Ph.D. degrees from Shanghai Jiao Tong University. He specializes in management information systems, operations research and scheduling.