Recommending knowledgeable people in a work-integrated learning system
Author(s) -
Günter Beham,
Barbara Kump,
Tobias Ley,
Stefanie Lindstaedt
Publication year - 2010
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2010.08.003
Subject(s) - computer science , recommender system , domain (mathematical analysis) , interpersonal communication , process (computing) , service (business) , work (physics) , order (exchange) , knowledge management , interpersonal influence , user modeling , domain knowledge , human–computer interaction , world wide web , user interface , psychology , mechanical engineering , engineering , social psychology , mathematical analysis , mathematics , economy , finance , economics , operating system
According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquire knowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expert finding systems can support the process of identifying knowledgeable colleagues thus facilitating communication and collaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed that represents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegrated learning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on an underlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on the basis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal help seeking at workplaces
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