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Updated: Jun 17, 2026

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TQR : Modelling user temporal preference for effective question routing.

Jia Xu1, Zhengkai Li2, Pin Lv3

  • 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510555, GuangDong, China; Key Laboratory of Cyberspace Security Defense (Institute of Information Engineering, Chinese Academy of Sciences), Beijing, 100085, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces TQR, a novel deep neural network for question routing (QR). TQR effectively routes questions by incorporating user access and asker acceptance temporal preferences, improving answer quality.

Keywords:
Community question answeringDeep neural networksQuestion routingUser modellingUser temporal preferences

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Area of Science:

  • Information Retrieval
  • Machine Learning
  • Artificial Intelligence

Background:

  • Existing question routing (QR) methods struggle to capture user access and asker acceptance temporal preferences.
  • These temporal preferences are crucial for directing questions to high-quality answerers in community question answering (CQA) systems.

Purpose of the Study:

  • To introduce a novel deep neural network model, TQR, for effective question routing.
  • To address the limitations of existing QR methods by incorporating user access and asker acceptance temporal preferences.
  • To enhance the accuracy and efficiency of question routing in CQA platforms.

Main Methods:

  • Developed TQR, a deep neural network model incorporating temporal preference information.
  • Designed an access temporal preference encoder to model users' periodic and evolving access time patterns.
  • Proposed an acceptance temporal preference encoder to learn long- and short-term asker preferences for answer submission times.

Main Results:

  • The TQR model demonstrated significant improvements in question routing.
  • Achieved an average improvement of 7.22% in Mean Reciprocal Rank (MRR) across six public datasets.
  • Outperformed existing state-of-the-art baseline methods in question routing tasks.

Conclusions:

  • TQR effectively utilizes temporal preference information for optimized question routing.
  • This research is the first to model asker acceptance temporal preferences for enhancing QR.
  • The proposed model offers a promising approach for improving the quality of answers in CQA systems.