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.
A collection of other related work REF has developed personalized models of asker satisfaction to predict whether a particular question starter will be satisfied with the answers given by others.
You%ve Got Answers: Towards Personalized Models for Predicting Success in Community Question Answering
Annual Meeting Of The Association For Computational Linguistics
Computer Science