The method of finding high-quality answers has a significant impact on users ’ satisfaction in a community question answering system. However, due to the lexical gap between questions and answers as well as spam typically contained in user-generated content, filtering and ranking answers is very challenging. Existing solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Meanwhile, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of links, i.e. positive links indicating high-quality answers, negative links indicating incorrect answers or user-generated spam, and propose an analogical reasoning-based approach which measures the analogy between the new question-answer linkages and those of some previous relevant knowledge which contains only positive links; the candidate answer which has the most analogous link to the supporting set is assumed to be the best answer. We conducted our experiments based on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of our proposed approach.

How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that this measure is formally related to a machine learning method known as Bayesian Sets. Building on this connection, we derive an analytic expression for the representativeness of objects described by a sparse vector of binary features. We then apply this measure to a large database of images, using it to determine which images are the most representative members of different sets. Comparing the resulting predictions to human judgments of representativeness provides a test of this measure with naturalistic stimuli, and illustrates how databases that are more commonly used in computer vision and machine learning can be used to evaluate psychological theories. 1