Ranking using multi-features in blog search

Abstract

Blog has received lots of attention since the revolution of Web 2.0 and has attracted millions of users to publish information on it. As time goes by, information seeking in this new media becomes an emergent issue. In our paper, we take multiple features unique in blogs into account and propose a novel algorithm to rank the blog posts in blog search. Coherence between the query type and blogger interest, document relevance and freshness are combined linearly to produce the final ranking score of a post. Specifically, we introduce a user modeling method to capture interests of bloggers. In our experiments, we invite volunteers to complete several tasks and their time cost in the tasks is taken as the primary criteria to evaluate the performance. The experimental results show that our algorithm outperforms traditional ones. © Springer-Verlag Berlin Heidelberg 2007.