ColdRoute: effective routing of cold questions in stack exchange sites
ColdRoute: effective routing of cold questions in stack exchange sites
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DOI：
10.1007/s10618-018-0577-7
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发表时间：
2018-06
影响因子：
4.8
通讯作者：
Jiankai Sun-;Abhinav Vishnu;Aniket Chakrabarti;Charles Siegel;S. Parthasarathy
中科院分区：
文献类型：
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作者：
Jiankai Sun-;Abhinav Vishnu;Aniket Chakrabarti;Charles Siegel;S. Parthasarathy
在社区问答服务（如Stack Exchange站点）中路由问题是一个得到充分研究的问题。然而，当发布新问题时观察到的冷启动现象并没有被现有的方法很好地解决。此外，新提问者提出的冷问题对最先进的方法提出了重大挑战。我们建议ColdRoute来应对这些挑战。ColdRoute能够处理将新老提问者发布的冷问题路由到匹配专家的任务。具体来说，我们在关键特征（如问题标签）的单热编码上使用Factorization Machines，并将我们的方法与CQARank和语义匹配（LDA、BoW和Doc2Vec）等得到充分研究的技术进行比较。使用来自8个堆栈交换站点的数据，我们能够在最先进的模型（如语义匹配）上改进路由度量（Precision@1, Accuracy， MRR），对于现有提问者发布的冷问题，分别提高159.5%，31.84和40.36%，对于新提问者发布的冷问题，分别提高123.1,27.03和34.81%。
Routing questions in Community Question Answer services 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.