The Novel approach can yield high levels of performance and nicely complements traditional question answering techniques driven by information extraction. In order for question answering systems to benet from this vast store of useful knowledge, they must copy with large volumes of useless data. Question Answering systems (QA) uses similarity in questions and ranking the relevant answer to user. The web gives large data and that require more time as well as no relevancy in answers. To solve this problem proposed system proposed novel Pair wise Learning to rANk model i.e PLANE which can quantitatively rank answer candidates from the relevant question pool. Specially, it uses two components i.e online learning component and one online search component. Our model is effective as well as achieves better performance than several existing questions answer selection system. User gets recommendation based on his profile. User recommend the new question to his friend and this is trust analysis so user can get top recommendation of newly arrived question of languages.

Answer Selection, Community-based Question Answering, Question-Answer pairs, Pair wise learning technique

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