Enhancing User Experience in User-Generated Content Websites by Exploiting Wikipedia

Abstract

We have witnessed the incredible popularity of user-generated contents (UGC) over the last few years. Benefited from UGC, current Web is vastly enriched by articles, photos or videos created by ordinary users. In this thesis, we target on three problems. First we have proposed to represent UGC from different domains in the same Wikipedia space. Given the uniform representation, cross domain queries and search are supported. Second, we have designed a Wikipedia based method to organize and manage the information flow in the websites. In this method, contents on similar topics are grouped together so that users are able to easily identify newsfeeds that they are interested at. Finally, we have investigated the problem of user tag prediction. We offer a unified framework that takes into account the frequency, temporal, correlation and social information. Then based on the framework, we build a promising user tag prediction model.