DocumentCode :
3717192
Title :
Recommending forum posts to designated experts
Author :
Jason H.D. Cho;Yanen Li;Roxana Girju;Chengxiang Zhai
Author_Institution :
University of Illinois at Urbana-Champaign
fYear :
2015
Firstpage :
659
Lastpage :
666
Abstract :
There are users who generate significant amounts of domain knowledge in online forums or community question and answer (CQA) websites. Existing literature defines them as `experts.´ These users attain such statuses by providing multiple relevant answers to the question askers. Past works have focused on recommending relevant posts to these users. With the rise of web forums where certified experts answer questions, strategies that are tailored towards addressing the new type of experts will be beneficial. In this paper, we identify a new type of user called `designated experts´ (i.e., users designated as domain experts by the web administrators). These are the experts who are guaranteed by web administrators to be an expert in a given domain. Our focus is on how we can capture the unique behavior of designated experts in an online domain. We have noticed designated experts have different behaviors compared to CQA experts. In particular, unlike existing CQAs, only one designated expert responds to any given thread. To capture this intuition, we introduce a matrix factorization algorithm with regularization to capture the behavior. Our results show that the regularization method improves the performance significantly compared to the baseline approach.
Keywords :
"Recommender systems","Message systems","Collaboration","Medical services","Big data","Semantics","Probabilistic logic"
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BigData.2015.7363810
Filename :
7363810
Link To Document :
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