Computers in Industry - Special double issue: WET ICE '95
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Just talk to me: a field study of expertise location
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Matchmaking for autonomous agents in electronic marketplaces
Proceedings of the fifth international conference on Autonomous agents
Recommending collaboration with social networks: a comparative evaluation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Relational View of Information Seeking and Learning in Social Networks
Management Science
IEEE Transactions on Knowledge and Data Engineering
Searching for expertise in social networks: a simulation of potential strategies
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Knowledge Management Strategies: Toward a Taxonomy
Journal of Management Information Systems
Expert Recommender: Designing for a Network Organization
Computer Supported Cooperative Work
Linked data on the web (LDOW2008)
Proceedings of the 17th international conference on World Wide Web
CI-KNOW: recommendation based on social networks
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Flexible Semantic-Based Service Matchmaking and Discovery
World Wide Web
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Improving the accuracy of job search with semantic techniques
BIS'07 Proceedings of the 10th international conference on Business information systems
Collaborative filtering recommender systems
The adaptive web
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Hybrid systems for personalized recommendations
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Proceedings of the 7th ACM conference on Recommender systems
Recommending program committee candidates for academic conferences
Proceedings of the 2013 workshop on Computational scientometrics: theory & applications
Using formal concept analysis for ontology maintenance in human resource recruitment
APCCM '13 Proceedings of the Ninth Asia-Pacific Conference on Conceptual Modelling - Volume 143
User Modeling and User-Adapted Interaction
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Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.