1. Question Routing in Community Question Answering: Putting Category in Its Place 1 The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2 AT&T Labs Research, San Francisco, CA,USA {bcli, king, lyu}@cse.cuhk.edu.hk irwin@research.att.comlyu}@cse.cuhk.edu.hk Baichuan Li 1, Irwin King 12, and Michael R. Lyu 1 Question Routing Many questions in CQA services are not solved timely Routing new posted questions to users who are most likely to give answers in a short period of time Appropriate users: expertise estimation Language Models (LMs) Problem Irrelevant information For an answerer, a complete set of questions the answerer has answered is utilized in the models Question category Screening irrelevant questions of an answerer Enhancing the efficiency of expertise estimation ii. Transferred Category-Sensitive LM II. Models (cont.)I. Motivations II. Models ~ An investigation of applying question category to question routing in CQA services Two simple but efficient category-sensitive LMs were proposed for estimating answerer expertise Results of experiments have proven that higher accuracies with lower costs are achieved due to the inclusion of question category IV. Experiments V. Conclusions Data Crawled from Computers & Internet and Entertainment & Music categories of Yahoo! Answers Platform PC with 2.4 GHz dual-core CPU, 3G memory Home Computers & Internet Entertainment & Music Software Internet Facebook Google Programming & Design Music Movies Blues ClassicalCountry i. Basic Category-Sensitive LM the question texts of all previously answered questions in c j for u i the leaf category of the new question q r Disadvantages of BCS-LM Based on the same-leaf-category assumption, with potential answerers under similar leaf categories being omitted Answerers with expertise in “Programming & Design” may also be an expert on questions asked in “Software” Estimating category-category transferring probability Answerer-based approach If there are many same answerers posting answers in two categories, these two categories should be similar with each other Category-answerer matrix E Each row represents one leaf category Each column represents one answer e ji : the number of answers u i provided in category c j iii. Compared Models Cluster-based LM (Zhou et al. 2009) Similar questions under same topic are clustered Each leaf category could be treated as a cluster Answerer expertise is estimated through calculating answerer’s contribution to each cluster and the similarity between the routed question and each cluster Mixture of LDA and QLLM (Liu et al. 2010) latent topics VS explicit categories MethodMRRMAPMQRT QLLM 0.14600.107010.4271 BCS-LM 0.1893 (↑29.66%)0.1424 (↑33.08%)5.5098 TCS-LM 0.1965 (↑34.59%)0.1469 (↑37.29%)8.9884 CBLM 0.00310.002416.7689 LDALM 0.1695 (↑16.10%)0.1281 (↑19.72%)4.2488 Figure 2. Different methods’ Prec@K in QR versus various Ks Figure 1. Part of category hierarchy in Yahoo! Answers Higher Accuracies + Lower Costs Table 2. MRR, MAP and MQRT of various methods Table 1. Description of data set