Annotation-aware web clustering based on topic model and random walks

Web page clustering based on semantic or topic promises improved search and browsing on the web. Intuitively, tags from social bookmarking websites such as del.icio.us can be used as a complementary source to document thus improving clustering of web pages. In this paper, we present a novel model which employs topic model to associate annotated document with a distribution of topics, and then constructs a graph including tags, document and topics by performing a Random Walks for clustering. We examine the performance of our model on a real-world data set, illustrating that our model provides improved clustering performance than algorithm utilizing page text alone.