Abstract:

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 systems in general and Yahoo! Answers in particular, all 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.