2011 IWanttoAnswerWhoHasaQuestionYah

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Notes

Yahoo! Answers is currently one of the most popular question answering systems. We claim however that its user experience could be significantly improved if it could route the "right question” to the "right user”. Indeed, while some users would rush answering a question such as "what should I wear at the prom?", others would be upset simply being exposed to it. We argue here that Community Question Answering sites in general and Yahoo! Answers in particular, need a mechanism that would expose users to questions they can relate to and possibly answer.

We propose here to address this need via a multi-channel recommender system technology for associating questions with potential answerers on Yahoo! Answers. One novel aspect of our approach is exploiting a wide variety of content and social signals users regularly provide to the system and organizing them into channels. Content signals relate mostly to the text and categories of questions and associated answers, while social signals capture the various user interactions with questions, such as asking, answering, voting, etc. We fuse and generalize known recommendation approaches within a single symmetric framework, which incorporates and properly balances multiple types of signals according to channels. Tested on a large scale dataset, our model exhibits good performance, clearly outperforming standard baselines.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 IWanttoAnswerWhoHasaQuestionYah	Yehuda Koren Gideon Dror Yoelle Maarek Idan Szpektor			I Want to Answer; Who Has a Question?: Yahoo! Answers Recommender System				10.1145/2020408.2020582		2011