Task complexity affects information seeking and use
Information Processing and Management: an International Journal
Social translucence: an approach to designing systems that support social processes
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
The New Review of Information Behaviour Research
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Collaborative Support for Informal Information in Collective Memory Systems
Information Systems Frontiers
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
Issues of context in information retrieval (IR): an introduction to the special issue
Information Processing and Management: an International Journal - Issues of context in information retrieval
DEMOIR: A Hybrid Architecture for Expertise Modeling and Recommender Systems
WETICE '00 Proceedings of the 9th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Relational View of Information Seeking and Learning in Social Networks
Management Science
The Turn: Integration of Information Seeking and Retrieval in Context (The Information Retrieval Series)
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
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
Measuring online information seeking context, Part 1: Background and method
Journal of the American Society for Information Science and Technology
Broad expertise retrieval in sparse data environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Searching for experts in the enterprise: combining text and social network analysis
Proceedings of the 2007 international ACM conference on Supporting group work
That's what friends are for: facilitating 'who knows what' across group boundaries
Proceedings of the 2007 international ACM conference on Supporting group work
The CSIRO enterprise search test collection
ACM SIGIR Forum
Inside the source selection process: Selection criteria for human information sources
Information Processing and Management: an International Journal
Pick me!: link selection in expertise search results
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-aspect expertise matching for review assignment
Proceedings of the 17th ACM conference on Information and knowledge management
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
PodCred: a framework for analyzing podcast preference
Proceedings of the 2nd ACM workshop on Information credibility on the web
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Describing and predicting information-seeking behavior on the Web
Journal of the American Society for Information Science and Technology
Enhancing Expert Finding Using Organizational Hierarchies
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Design and Evaluation of a University-Wide Expert Search Engine
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
A user-oriented model for expert finding
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Tool support for technology scouting using online sources
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
Similar researcher search in academic environments
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Foundations and Trends in Information Retrieval
Finding the right supervisor: expert-finding in a university domain
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Ranking experts using author-document-topic graphs
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Supporting exploratory people search: a study of factor transparency and user control
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Benchmarking domain-specific expert search using workshop program committees
Proceedings of the 2013 workshop on Computational scientometrics: theory & applications
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Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-centered perspective. The main focus has been on developing content-based algorithms similar to document search. These algorithms identify matching experts primarily on the basis of the textual content of documents with which experts are associated. Other factors, such as the ones identified by expertise-seeking models, are rarely taken into account. In this article, we extend content-based expert-finding approaches with contextual factors that have been found to influence human expert finding. We focus on a task of science communicators in a knowledge-intensive environment, the task of finding similar experts, given an example expert. Our approach combines expertise-seeking and retrieval research. First, we conduct a user study to identify contextual factors that may play a role in the studied task and environment. Then, we design expert retrieval models to capture these factors. We combine these with content-based retrieval models and evaluate them in a retrieval experiment. Our main finding is that while content-based features are the most important, human participants also take contextual factors into account, such as media experience and organizational structure. We develop two principled ways of modeling the identified factors and integrate them with content-based retrieval models. Our experiments show that models combining content-based and contextual factors can significantly outperform existing content-based models. © 2010 Wiley Periodicals, Inc. This is an expanded and revised version of Hofmann, Balog, Bogers, & de Rijke, [2008].