Time-aware hybrid expertise retrieval system in community question answering services

Article Type

Research Article

Publication Title

Applied Intelligence

Abstract

This paper introduces a time-aware hybrid expertise retrieval (TaHER) system for community question answering (CQA) services. It comprises of a text-based part and a network-based part. The text-based part makes use of the textual and the temporal information associated with questions and answers. Moreover, it assesses the recent interests and the activities of answerers. For a given question, it determines the knowledge of each answerer and identify active answerers with adequate knowledge. The network-based part is composed of several period-dependent networks. It uses the relationships among the answerers along with temporal information. Next, it applies a link analysis technique on the networks to determine the time-aware authority of each answerer in the community. We, nonetheless, propose a fusion strategy for combining the offshoots of these two parts. Using 5 performance measures, TaHER system is compared with 20 state-of-the-art algorithms on 4 real-world datasets. According to our experiments, in 93.75% (375 out of 400) cases, the proposed approach outperforms the comparing approaches. We also experimentally validate the importance of each assumption used by us.

First Page

6914

Last Page

6931

DOI

10.1007/s10489-020-02177-2

Publication Date

10-1-2021

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