TQR : Modelling user temporal preference for effective question routing

Question routing (QR) aims to route questions to answerers who are likely to provide high-quality answers. Though existing QR methods have achieved promising results, they still face two key challenges that have not yet been well addressed: 1) user access temporal preference (i.e., user preference to the time of accessing community question answering websites) has not been well captured and utilized and 2) asker acceptance temporal preference (i.e., asker preference to an answer's submission time) is neglected. Given this, we introduce a novel deep neural network model named TQR which applies temporal preference information for effective question routing. To address the first challenge, we design an access temporal preference encoder in TQR that models the access temporal preferences of users based on their periodic and evolving patterns of accessing time. To solve the second challenge, an acceptance temporal preference encoder is proposed in TQR which learns long&short-term acceptance temporal preferences of askers. Then, an answerer's representation is computed based on the learned user access temporal preferences and asker acceptance preferences. Finally, a question is routed to those answerers whose representations better match the question representation. To the best of our knowledge, this is the first attempt to model the acceptance temporal preferences of askers to optimize the QR task. Extensive experiments are conducted on six public datasets and the experimental results show that the proposed TQR model achieves an average improvement of 7.22% in MRR compared to those best baselines.