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Enhanced models for expertise retrieval using community-aware strategies.
Hongbo Deng1, Irwin King, Michael R Lyu
1Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, IL 61801-2302, USA. hbdeng@uiuc.edu
Summary
This study introduces community-aware strategies to improve expertise retrieval by incorporating community information. The new methods enhance expert suggestion accuracy by considering social networks and document context.
Area of Science:
- Information Retrieval
- Computer Science
- Social Network Analysis
Background:
- Expertise retrieval systems typically focus on documents, overlooking valuable community affiliations.
- Existing methods lack the ability to leverage community insights for more accurate expert suggestions.
Purpose of the Study:
- To develop and evaluate community-aware strategies for enhancing expertise retrieval.
- To address the limitations of previous algorithms by integrating community information.
Main Methods:
- Proposed a novel smoothing method using community context for statistical language modeling to improve document relevance.
- Developed a query-sensitive AuthorRank algorithm leveraging community coauthorship networks to model author authority.
- Introduced an adaptive ranking refinement method for enhanced expertise retrieval.
Main Results:
- Both community-aware strategies significantly improved expertise retrieval performance.
- The enhanced models demonstrated effectiveness and robustness in identifying relevant experts.
- Experimental results confirmed consistent and significant improvements over existing methods.
Conclusions:
- Integrating community information is crucial for advancing expertise retrieval systems.
- Community-aware strategies offer a promising direction for more accurate and effective expert suggestion.
- The proposed methods provide a robust framework for leveraging social and contextual data in information retrieval.
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