Analyzing bias in CQA-based expert finding test sets

No abstract is available for this paper.

• Publication year

  2014

• Venue

  Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

• Publication date

  2014-07-03

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/2600428.2609486
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Expertise Retrieval

  2012cited by this paper
• Identifying authoritative actors in question-answering forums: the case of Yahoo! answers

  2008influential reference
• Expertise networks in online communities: structure and algorithms

  2007cited by this paper
• Finding experts in community-based question-answering services

  2005cited by this paper

• Efficient and Accurate Non-Metric k-NN Search with Applications to Text Matching

  2022cites this paper
• An Analysis on User Behaviors in Online Question and Answering Communities

  2020cites this paper
• Question Retrieval based on Community Question Answering Baseline Selection among Retrieval Models on two Datasets

  2019cites this paper
• Finding of experts using behavioral aspects

  2018cites this paper
• Cross-Platform Question Answering in Social Networking Services

  2017cites this paper
• Crowd Knowledge Creation Acceleration

  2017cites this paper
• Effective and Efficient Approaches to Retrieving and Using Expertise in Social Media

  2016cites this paper
• A Comprehensive Survey and Classification of Approaches for Community Question Answering

  2016cites this paper
• Learning to Find Topic Experts in Twitter via Different Relations

  2016cites this paper
• Estimating Domain-Specific User Expertise for Answer Retrieval in Community Question-Answering Platforms

  2016cites this paper
• Modeling Temporal Behavior to Identify Potential Experts in Question Answering Communities

  2016cites this paper
• Incorporating Distinct Opinions in Content Recommender System

  2015cites this paper
• Influence Analysis by Heterogeneous Network in MOOC Forums: What can We Discover?

  2015cites this paper