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Formulation of a hybrid expertise retrieval system in community question answering services

Published: 01 February 2019 Publication History

Abstract

In this paper, we propose a hybrid expertise retrieval system for community question answering services. The proposed system consists of two segments: a text based segment and a network based segment. For a given question, the text based segment estimates users' knowledge introducing two new concepts: question hardness and question answerer association. The network based segment, moreover, incorporates users' relative performances into the network structure. We denote the outputs of these two segments as knowledge score and authority score, respectively. We aggregate these two scores using a fusion technique to quantify the expertise of a given user for a given question. We have generated four datasets by downloading questions and answers from Yahoo! Answers. The performance of the proposed system is found to be superior than that of 18 state-of-the-art algorithms on these four real-world datasets.

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  • (2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
  • (2024)Towards Robust Expert Finding in Community Question Answering PlatformsAdvances in Information Retrieval10.1007/978-3-031-56069-9_12(152-168)Online publication date: 24-Mar-2024
  • (2023)Value-Wise ConvNet for Transformer Models: An Infinite Time-Aware Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321923135:10(9932-9945)Online publication date: 1-Oct-2023
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Information & Contributors

Information

Published In

cover image Applied Intelligence
Applied Intelligence  Volume 49, Issue 2
February 2019
534 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2019

Author Tags

  1. Answer quality
  2. Community question answering
  3. Expertise retrieval
  4. Language model
  5. Question hardness
  6. Social network analysis

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View all
  • (2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
  • (2024)Towards Robust Expert Finding in Community Question Answering PlatformsAdvances in Information Retrieval10.1007/978-3-031-56069-9_12(152-168)Online publication date: 24-Mar-2024
  • (2023)Value-Wise ConvNet for Transformer Models: An Infinite Time-Aware Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321923135:10(9932-9945)Online publication date: 1-Oct-2023
  • (2023)ExpRec: Deep knowledge-awared question routing in software question answering communityApplied Intelligence10.1007/s10489-022-03369-853:5(5681-5696)Online publication date: 1-Mar-2023
  • (2022)MMKRL: A robust embedding approach for multi-modal knowledge graph representation learningApplied Intelligence10.1007/s10489-021-02693-952:7(7480-7497)Online publication date: 1-May-2022
  • (2021)Multimodal emotion recognition with hierarchical memory networksIntelligent Data Analysis10.3233/IDA-20518325:4(1031-1045)Online publication date: 1-Jan-2021
  • (2021)Time-aware hybrid expertise retrieval system in community question answering servicesApplied Intelligence10.1007/s10489-020-02177-251:10(6914-6931)Online publication date: 1-Oct-2021
  • (2020)Towards Question-based High-recall Information RetrievalACM Transactions on Information Systems10.1145/338864038:3(1-35)Online publication date: 18-May-2020
  • (2019)Finding Active Experts for Question Routing in Community Question Answering ServicesPattern Recognition and Machine Intelligence10.1007/978-3-030-34872-4_36(320-327)Online publication date: 17-Dec-2019

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