Subtopic Deep Dive
Expertise Retrieval in Community Question Answering
Research Guide
What is Expertise Retrieval in Community Question Answering?
Expertise Retrieval in Community Question Answering identifies users with domain-specific knowledge in CQA platforms like Stack Overflow using algorithms for profiling and question-expert matching.
This subtopic develops models to rank experts based on past answers and user interactions in CQA sites. Key works include Riahi et al. (2012) with 195 citations on finding expert users and Fang et al. (2016) with 89 citations using heterogeneous social networks. Over 20 papers since 2012 address expertise ranking and matching challenges.
Why It Matters
Expertise retrieval improves answer quality and response times in CQA platforms handling millions of questions yearly, as shown by Riahi et al. (2012) on Stack Overflow expert identification. It supports scalable knowledge sharing in forums like Yahoo Answers, reducing unanswered questions per Chua and Banerjee (2015) analysis of Stack Overflow. Applications include enterprise Q&A systems and recommendation engines, with Zhao et al. (2017) demonstrating multi-faceted ranking for better expert matching.
Key Research Challenges
Asymmetric Relationship Modeling
Directed graphs in CQA require preserving asymmetric transitivity between questions, answers, and users. Sun et al. (2019) introduce ATP embedding to handle this in CQA graphs. Traditional symmetric methods fail on heterogeneous networks.
Expert Profiling Accuracy
Extracting reliable expertise from noisy user histories remains difficult. Riahi et al. (2012) profile experts via topic modeling but overlook temporal dynamics. Roy et al. (2022) review ML/DL limitations in handling sparse data.
Question-Expert Matching
Matching new questions to suitable experts faces semantic gaps. Das et al. (2016) use Siamese Networks for similar question retrieval as a proxy. Fang et al. (2016) highlight challenges in heterogeneous ranking.
Essential Papers
Finding expert users in community question answering
Fatemeh Riahi, Zainab Zolaktaf, Mahdi Shafiei et al. · 2012 · 195 citations
Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most ...
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...
Together we stand: Siamese Networks for Similar Question Retrieval
Arpita Das, Harish Yenala, Manoj Kumar Chinnakotla et al. · 2016 · 79 citations
Community Question Answering (cQA) services like Yahoo! Answers 1 , Baidu Zhidao 2 , Quora 3 , StackOverflow 4 etc. provide a platform for interaction with experts and help users to obtain precise ...
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning
Zhou Zhao, Hanqing Lu, Vincent W. Zheng et al. · 2017 · Proceedings of the AAAI Conference on Artificial Intelligence · 55 citations
Nowadays the community-based question answering (CQA) sites become the popular Internet-based web service, which have accumulated millions of questions and their posted answers over time. Thus, que...
Answers or no answers: Studying question answerability in Stack Overflow
Alton Y.K. Chua, Snehasish Banerjee · 2015 · Journal of Information Science · 49 citations
Some questions posted in community question answering sites (CQAs) fail to attract a single answer. To address the growing volumes of unanswered questions in CQAs, the objective of this paper is tw...
Credibility-inspired ranking for blog post retrieval
Wouter Weerkamp, Maarten de Rijke · 2012 · Information Retrieval · 47 citations
Credibility of information refers to its believability or the believability of its sources. We explore the impact of credibility-inspired indicators on the task of blog post retrieval, following th...
Reading Guide
Foundational Papers
Start with Riahi et al. (2012) for core expert profiling in CQA, then Weerkamp and de Rijke (2012) for credibility ranking, and Ravi et al. (2014) for question quality impacts.
Recent Advances
Study Roy et al. (2022) for ML/DL review, Sun et al. (2019) for ATP embeddings, and Zou and Kanoulas (2020) for question-based retrieval extensions.
Core Methods
Core techniques: topic modeling and language models (Riahi et al., 2012), Siamese Networks (Das et al., 2016), heterogeneous ranking (Fang et al., 2016; Zhao et al., 2017), directed graph embeddings (Sun et al., 2019).
How PapersFlow Helps You Research Expertise Retrieval in Community Question Answering
Discover & Search
Research Agent uses searchPapers with query 'expertise retrieval community question answering' to find Riahi et al. (2012), then citationGraph reveals 195 citing papers and findSimilarPapers uncovers Fang et al. (2016). exaSearch drills into Stack Overflow datasets for unpublished extensions.
Analyze & Verify
Analysis Agent applies readPaperContent on Zhao et al. (2017) to extract ranking metrics, verifyResponse with CoVe checks claims against Roy et al. (2022) review, and runPythonAnalysis reproduces ATP embeddings from Sun et al. (2019) using NumPy for transitivity preservation. GRADE scores evidence strength on expert profiling methods.
Synthesize & Write
Synthesis Agent detects gaps like temporal expertise decay post-Riahi et al. (2012), flags contradictions between symmetric and asymmetric models. Writing Agent uses latexEditText for equations, latexSyncCitations for 10+ papers, latexCompile for report, and exportMermaid diagrams heterogeneous CQA networks.
Use Cases
"Reproduce ATP embedding results from Sun et al. 2019 on Stack Overflow data"
Research Agent → searchPapers 'ATP directed graph CQA' → Analysis Agent → readPaperContent → runPythonAnalysis (NumPy/pandas on graph data) → matplotlib plots of transitivity metrics.
"Write a survey section on Siamese Networks for CQA expert matching"
Research Agent → findSimilarPapers 'Das 2016 Siamese' → Synthesis → gap detection → Writing Agent → latexEditText draft → latexSyncCitations (Das et al., Fang et al.) → latexCompile PDF.
"Find GitHub repos implementing heterogeneous network learning for CQA"
Research Agent → citationGraph 'Fang 2016' → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect (Fang/Wu code) → exportCsv implementations.
Automated Workflows
Deep Research workflow scans 50+ CQA papers via searchPapers → citationGraph → structured report on expertise trends from Riahi (2012) to Roy (2022). DeepScan applies 7-step analysis with CoVe verification on Sun et al. (2019) ATP, checkpointing graph embeddings. Theorizer generates hypotheses on DL improvements over Fang et al. (2016) networks.
Frequently Asked Questions
What defines Expertise Retrieval in CQA?
It identifies knowledgeable users in platforms like Stack Overflow via algorithms analyzing answer history and interactions (Riahi et al., 2012).
What are main methods used?
Methods include topic modeling (Riahi et al., 2012), heterogeneous networks (Fang et al., 2016), Siamese Networks (Das et al., 2016), and ATP embeddings (Sun et al., 2019).
What are key papers?
Riahi et al. (2012, 195 citations) on expert finding; Fang et al. (2016, 89 citations) on social networks; Zhao et al. (2017, 55 citations) on ranking.
What open problems exist?
Challenges include temporal expertise shifts, sparse data in new domains (Roy et al., 2022), and scalable asymmetric matching beyond ATP (Sun et al., 2019).
Research Expert finding and Q&A systems with AI
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Part of the Expert finding and Q&A systems Research Guide