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Title: ColdRoute: effective routing of cold questions in stack exchange sites

Journal Article · · Data Mining and Knowledge Discovery
ORCiD logo [1];  [2];  [3];  [2];  [1]
  1. The Ohio State Univ., Columbus, OH (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Microsoft, Albuquerque, NM (United States)

Routing questions in Community Question Answer services such as Stack Exchange sites is a well-studied problem. Yet, cold-startā€”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). Furthermore by 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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
CCF-1645599; IIS-1550302; CNS-1513120; PAS0166; AC05-76RL01830
OSTI ID:
1460547
Journal Information:
Data Mining and Knowledge Discovery, Vol. 32, Issue 5; ISSN 1384-5810
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 11 works
Citation information provided by
Web of Science

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