The tags of news articles give readers the most important and relevant information regarding the news articles, which are more useful than a simple bag of keywords extracted from news articles. Moreover, latent dependency among tags can be used to assign tags with different weight. Traditional content-based recommendation engines have largely ignored the latent dependency among tags. To solve this problem, we implemented a prototype system called PRST, which is presented in this paper. PRST builds a tag dependency graph to capture the latent dependency among tags. The demonstration shows that PRST makes news recommendation more effectively.
展开▼
