Subtopic Deep Dive
Question Routing in Expert Finding Systems
Research Guide
What is Question Routing in Expert Finding Systems?
Question routing in expert finding systems directs user queries to the most suitable experts based on topical expertise matching and relevance ranking.
This subtopic focuses on models that route questions in community question answering (CQA) systems to users with demonstrated expertise. Key methods include content-based matching and social network learning for improved response quality (Chang and Pal, 2013, 88 citations). Over 20 papers explore routing techniques, with foundational work emphasizing referral networks (McDonald and Ackerman, 2000, 426 citations).
Why It Matters
Question routing enhances answer quality and reduces response times in large CQA platforms like Stack Overflow by matching queries to experts. Chang and Pal (2013) show routing increases collaborative answering rates by 25% in Yahoo! Answers. McDonald and Ackerman (2000) demonstrate referral-based routing supports organizational knowledge sharing, impacting enterprise search systems. Fang et al. (2016) apply heterogeneous network learning to propagate expertise, improving recommendations in social Q&A sites.
Key Research Challenges
Expertise Inference Accuracy
Inferring latent expertise from sparse user activity remains difficult without direct evidence. Serdyukov (2009) highlights limitations of document-centric models in capturing indirect expertise signals. Modern systems struggle with cross-domain interests as noted in Roy et al. (2022).
Query-Expert Matching Scalability
Real-time routing in large-scale CQA sites requires efficient relevance ranking over millions of users. Chang and Pal (2013) report computational bottlenecks in feature-rich models for Yahoo! Answers. Expansion models like those in Arguello et al. (2021) add overhead for blog-like content.
Evaluation Metric Reliability
Standard metrics like response time fail to capture answer quality in routing evaluations. Ravi et al. (2014) emphasize question quality's role in routing success but lack standardized benchmarks. Fang et al. (2016) note inconsistencies in heterogeneous network propagation metrics.
Essential Papers
Expertise recommender
David W. McDonald, Mark S. Ackerman · 2000 · 426 citations
Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. Pe...
Just talk to me
David W. McDonald, Mark S. Ackerman · 1998 · 270 citations
Article Free Access Share on Just talk to me: a field study of expertise location Authors: David W. McDonald Department of Information and Computer Science, University of California, Irvine, Irvine...
Document Representation and Query Expansion Models for Blog Recommendation
Jaime Arguello, Jonathan L. Elsas, Jamie Callan et al. · 2021 · Proceedings of the International AAAI Conference on Web and Social Media · 111 citations
We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from t...
Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review
Pradeep Kumar Roy, Sunil Saumya, Jyoti Prakash Singh et al. · 2022 · CAAI Transactions on Intelligence Technology · 92 citations
Abstract Over the last couple of decades, community question‐answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and ...
Community-Based Question Answering via Heterogeneous Social Network Learning
Hanyin Fang, Fei Wu, Zhou Zhao et al. · 2016 · Proceedings of the AAAI Conference on Artificial Intelligence · 89 citations
Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and kn...
Routing questions for collaborative answering in community question answering
Shuo Chang, Aditya Pal · 2013 · 88 citations
Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the qu...
Searching Twitter: Separating the Tweet from the Chaff
Jonathan Hurlock, Max L. Wilson · 2021 · Proceedings of the International AAAI Conference on Web and Social Media · 77 citations
Within the millions of digital communications posted in online social networks, there is undoubtedly some valuable and useful information. Although a large portion of social media content is consid...
Reading Guide
Foundational Papers
Start with McDonald and Ackerman (2000) for referral-based expertise location, then Chang and Pal (2013) for direct CQA routing models, followed by Serdyukov (2009) on indirect evidence.
Recent Advances
Study Fang et al. (2016) for network propagation advances and Roy et al. (2022) for ML/DL review in CQAs.
Core Methods
Core techniques include vector space matching (Chang and Pal, 2013), graph propagation (Fang et al., 2016), and query expansion (Arguello et al., 2021).
How PapersFlow Helps You Research Question Routing in Expert Finding Systems
Discover & Search
Research Agent uses searchPapers and citationGraph to map routing literature from Chang and Pal (2013), revealing 88 downstream citations on CQA scalability. exaSearch uncovers niche papers like Serdyukov (2009) on indirect expertise, while findSimilarPapers links McDonald and Ackerman (2000) to modern enterprise applications.
Analyze & Verify
Analysis Agent applies readPaperContent to extract routing algorithms from Chang and Pal (2013), then verifyResponse with CoVe checks claims against Fang et al. (2016). runPythonAnalysis recreates expertise matching with pandas on citation data, graded via GRADE for statistical significance in performance lifts.
Synthesize & Write
Synthesis Agent detects gaps in scalability evaluations post-Chang and Pal (2013), flagging contradictions between direct (McDonald and Ackerman, 2000) and indirect (Serdyukov, 2009) evidence. Writing Agent uses latexEditText, latexSyncCitations for arXiv-ready reviews, and latexCompile for diagrams via exportMermaid of routing flowcharts.
Use Cases
"Reproduce routing accuracy metrics from Chang and Pal 2013 on modern CQA datasets"
Research Agent → searchPapers('question routing CQA') → Analysis Agent → runPythonAnalysis(pandas on extracted features) → GRADE-verified precision/recall plots.
"Write a survey section on expertise routing with citations to McDonald Ackerman and Serdyukov"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations → latexCompile to PDF with bibliography.
"Find GitHub repos implementing heterogeneous network routing like Fang et al 2016"
Research Agent → citationGraph(Fang 2016) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect for networkx code.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'question routing expert finding', producing structured reports with citation graphs from McDonald and Ackerman (2000). DeepScan applies 7-step CoVe analysis to Chang and Pal (2013), verifying routing metrics with runPythonAnalysis checkpoints. Theorizer generates hypotheses on hybrid direct-indirect models from Serdyukov (2009) and Fang et al. (2016).
Frequently Asked Questions
What is question routing in expert finding?
Question routing matches user queries to experts via topical relevance models in CQA systems (Chang and Pal, 2013).
What methods dominate question routing research?
Content-based matching (Chang and Pal, 2013), heterogeneous network learning (Fang et al., 2016), and indirect evidence models (Serdyukov, 2009) are primary approaches.
What are key papers on this topic?
Foundational: McDonald and Ackerman (2000, 426 citations), Chang and Pal (2013, 88 citations); recent: Fang et al. (2016, 89 citations), Roy et al. (2022, 92 citations).
What open problems exist in question routing?
Scalable real-time matching for cross-domain expertise and reliable quality metrics beyond response time persist (Serdyukov, 2009; Ravi et al., 2014).
Research Expert finding and Q&A systems with AI
PapersFlow provides specialized AI tools for Computer Science researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Code & Data Discovery
Find datasets, code repositories, and computational tools
Deep Research Reports
Multi-source evidence synthesis with counter-evidence
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Computer Science & AI use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Question Routing in Expert Finding Systems with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Computer Science researchers
Part of the Expert finding and Q&A systems Research Guide