Supplementary Q&A Recommendation Based on Transfer Learning Enhanced Multi-Label Multi-Document Classifier
[Objective]This paper proposes a recommendation method for supplementary question-and-answer(Q&A)based on a multi-label,multi-document Q&A classification model enhanced by transfer learning.It aims to identify and recommend supplementary answers in online Q&A communities.[Methods]We introduced new features alongside existing ones to classify the supplementary relationships between questions and answers.Then,we established a transfer learning-enhanced multi-label,multi-document classification model to identify and recommend supplementary answers.[Results]We conducted three meta-tasks on real datasets from the Zhihu community.The proposed method improves precision,recall,and Fl score by 48.29％,15.75％,and 32.53％,respectively,on average.[Limitations]The method was only applied to health-related Q&A topics in Zhihu and has yet to be validated across different platforms or topics.[Conclusions]The proposed recommendation method effectively recommends supplementary answers.It helps users in Q&A communities obtain more comprehensive answers and promote knowledge utilization within the community.
Q&A RecommendationSupplementary Relationship Between Q&AsFew-Shot ClassificationMulti-Label Multi-Document Classification
NETL
NSTL
万方数据
