Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Composing Questions through Conceptual Authoring
Computational Linguistics
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Incremental probabilistic latent semantic analysis for automatic question recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Efficient interactive fuzzy keyword search
Proceedings of the 18th international conference on World wide web
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Distributed question answering services, like Yahoo Answer and Aardvark, are known to be useful for end users and have also opened up numerous topics ranging in many research fields. In this paper, we propose a user-support tool for composing questions in such services. Our system incrementally recommends similar questions while users are typing their question in a sentence, which gives the users opportunities to know that there are similar questions that have already been solved. A question database is semantically analyzed and searched in the semantic space by boosting the performance of similarity searches with database techniques such as server/client caching and LSH (Locality Sensitive Hashing). The more text the user enters, the more similar the recommendations will become to the ultimately desired question. This unconscious editing-as-a-sequence-of-searches approach helps users to form their question incrementally through interactive supplementary information. Not only askers nor repliers, but also service providers have advantages such as that the knowledge of the service will be autonomously refined by avoiding for novice users to repeat questions which have been already solved.