  Community based question answering services have arisen as a popular
knowledge sharing pattern for netizens. With abundant interactions among users,
individuals are capable of obtaining satisfactory information. However, it is
not effective for users to attain answers within minutes. Users have to check
the progress over time until the satisfying answers submitted. We address this
problem as a user personalized satisfaction prediction task. Existing methods
usually exploit manual feature selection. It is not desirable as it requires
careful design and is labor intensive. In this paper, we settle this issue by
developing a new multiple instance deep learning framework. Specifically, in
our settings, each question follows a weakly supervised learning multiple
instance learning assumption, where its obtained answers can be regarded as
instance sets and we define the question resolved with at least one
satisfactory answer. We thus design an efficient framework exploiting multiple
instance learning property with deep learning to model the question answer
pairs. Extensive experiments on large scale datasets from Stack Exchange
demonstrate the feasibility of our proposed framework in predicting askers
personalized satisfaction. Our framework can be extended to numerous
applications such as UI satisfaction Prediction, multi armed bandit problem,
expert finding and so on.
