Expert Recommendation in Q&A Community Based on Topic Interest and Domain Authority
[Objective]This paper aims to enhance the accuracy of expert recommendations in Q&A communities based on topics of users'historical Q&A texts and contextual information.[Methods]First,we combined the BERT model with the Labeled-LDA model.Then,we utilized the label information to vectorize users'historical Q&A texts.Third,we identified contextual topics with dimension reduction and topic clustering.We also obtained the probability distribution of the expert's topic interests.Fourth,based on the results of topic interest mining,we constructed the Topic Sensitive PageRank Algorithm(TSPR).We used the users'quality weight to calculate their domain authority iteratively.From this,we proposed the TIDARank algorithm for expert recommendation.[Results]Based on the Stack Exchange public dataset,the BERT-LLDA model outperformed TF-IDF,BERT,and BERT-LDA models on silhouette coefficient(0.5756)and topic coherence(0.4766).The ACC@20 and MRR@20 of TIDARank reached 0.5807 and 0.2430,respectively,improved by 0.145 and 0.081 compared with the best-performing Bi-LSTM+TSPR baseline algorithm.[Limitations]We did not consider user activity in link analysis.[Conclusions]The BERT-LLDA model could optimize topic clustering for question-answering texts and improve the performances of expert recommendations in Q&A communities.
Community Question AnsweringExpert RecommendationBERTLabeled-LDAPageRank
NETL
NSTL
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