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Question answering communities such as Yahoo! Answers have emerged as a popular alternative to general-purpose web search. By directly interacting with other participants, information seekers can obtain specific answers to their questions. However, user success in obtaining satisfactory answers varies greatly. | You ve Got Answers Towards Personalized Models for Predicting Success in Community Question Answering YandongLiu and Eugene Agichtein Emory University yliu49 eugene @mathcs.emory.edu Abstract Question answering communities such as Yahoo Answers have emerged as a popular alternative to general-purpose web search. By directly interacting with other participants information seekers can obtain specific answers to their questions. However user success in obtaining satisfactory answers varies greatly. We hypothesize that satisfaction with the contributed answers is largely determined by the asker s prior experience expectations and personal preferences. Hence we begin to develop personalized models of asker satisfaction to predict whether a particular question author will be satisfied with the answers contributed by the community participants. We formalize this problem and explore a variety of content structure and interaction features for this task using standard machine learning techniques. Our experimental evaluation over thousands of real questions indicates that indeed it is beneficial to personalize satisfaction predictions when sufficient prior user history exists significantly improving accuracy over a one-size-fits-all prediction model. 1 Introduction Community Question Answering CQA has recently become a viable method for seeking information online. As an alternative to using generalpurpose web search engines information seekers now have an option to post their questions often complex specific and subjective on Community QA sites such as Yahoo Answers and have their questions answered by other users. Hundreds of millions of answers have already been posted for tens of millions of questions in Yahoo Answers. However the success of obtaining satisfactory answers in the available CQA portals varies greatly. In many cases the questions posted by askers go un-answered or are answered poorly never obtaining a satisfactory answer. In our recent work Liu et al. 2008 we
