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Expert recommendations with temporal dynamics of user interest in CQA
Xiaoqi LV
, Ke JI
, Zhenxiang CHEN
, Kun MA
, Jun WU
, Yidong LI
,
Guandong XU
Offices of the President (P)
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapters
1
Citation (Scopus)
Overview
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Keyphrases
Temporal Dynamics
100%
User Interest
100%
Community Question Answering
100%
Expert Recommendation
100%
Bidirectional Gated Recurrent Unit (BiGRU)
40%
Attention Mechanism
40%
Mechanism-based
40%
User Expertise
40%
User Questions
40%
Copyright
20%
Switzerland
20%
Knowledge Sharing
20%
Springer Nature
20%
Question Answering
20%
Social Platform
20%
Recommendation Accuracy
20%
State-of-the-art Techniques
20%
Experiment Results
20%
Content Information
20%
Recommendation Method
20%
Recommendation Model
20%
Feature Representation
20%
Recommendation Quality
20%
CNN-GRU
20%
Similarity Matching
20%
Essential Services
20%
Computer Science
Temporal Dynamic
100%
Convolutional Neural Network
66%
Attention (Machine Learning)
66%
Recommendation Accuracy
33%
Content Information
33%
Knowledge-Sharing
33%
Social Platform
33%