Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Community Question Ansring (CQA) has emerged as a popular forum for users to pose questions for other users to ansr. Over the last few years, CQA portals such as Naver and Yahoo! Ansrs have exploded in popularity, and now provide a viable alternative to general purpose b search. At the same time, the ansrs to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question ansring. Unfortunately, the quality of the submitted questions and ansrs varies widely - increasingly so that a large fraction of the content is not usable for ansring queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains.   address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality ansrs, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.
['Jiang Bian', 'Yandong Liu', 'Ding Zhou', 'Eugene Agichtein', 'Hongyuan Zha']
I am writing a research paper on a idea Learning to recognize reliable users and content in social media with coupled mutual reinforcement
[{"from": "human", "value": "I am writing a research paper on a idea Learning to recognize reliable users and content in social media with coupled mutual reinforcement"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Community Question Ansring (CQA) has emerged as a popular forum for users to pose questions for other users to ansr. Over the last few years, CQA portals such as Naver and Yahoo! Ansrs have exploded in popularity, and now provide a viable alternative to general purpose b search. At the same time, the ansrs to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question ansring. Unfortunately, the quality of the submitted questions and ansrs varies widely - increasingly so that a large fraction of the content is not usable for ansring queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains.   address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality ansrs, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods."}]