Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena observed when a new question is posted is not well addressed by existing approaches. Additionally, cold questions posted by new askers present significant challenges to state-of-the-art approaches. We propose ColdRoute to address these challenges. ColdRoute is able to handle the task of routing cold questions posted by new or existing askers to matching experts. Specifically, we use Factorization Machines on the one-hot encoding of critical features such as question tags and compare our approach to well-studied techniques such as CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159.5\%$,$31.84\%$, and $40.36\%$ for cold questions posted by existing askers, and $123.1\%$, $27.03\%$, and $34.81\%$ for cold questions posted by new askers respectively.

本文提出了ColdRoute方法，该方法使用因子分解机从关键特征的 one-hot 编码中提取问题标签来解决CQA中新问题的冷启动问题，该方法通过在八个Stack Exchange网站上的测试证明，与现有技术相比，可以使冷问题的路由指标（Precision@1，Accuracy，MRR）提高159.5％，31.84％和40.36％，可以使新提问者的冷问题的路由指标提高123.1％，27.03％和34.81％。

ColdRoute: Stack Exchange 网站中冷门问题的有效路由