MFCRank:: A Web ranking algorithm based on correlation of multiple features

This paper presents a new ranking algorithm MFCRank for topic-specific Web search systems. The basic idea is to correlate two types of similarity information into a unified link analysis model so that the rich content and link features in Web collections can be exploited efficiently to improve the ranking performance. First, a new surfer model JBC is proposed, under which the topic similarity information among neighborhood pages is used to weigh the jumping probability of the surfer and to direct the surfing activities. Secondly, as JBC surfer model is still query-independent, a correlation between the query and JBC is essential. This is implemented by the definition of MFCRank score, which is the linear combination of JBC score and the similarity value between the query and the matched pages. Through the two correlation steps, the features contained in the plain text, link structure, anchor text and user query can be smoothly correlated in one single ranking model. Ranking experiments have been carried out on a set of topic-specific Web page collections. Experimental results showed that our algorithm gained great improvement with regard to the ranking precision.