POPULARITY-BASED RELEVANCE PROPAGATION
It is evident that information resources on the World Wide Web (WWW) are growing rapidly with unpredictable rate. Under these circumstances, web search engines help users to find useful information. Ranking the retrieved results is the main challenge of every search engine. There are some ranking algorithms based on content and connectivity such as BM25 and PageRank. Due to low precision of these algorithms for ranking on the web, combinational algorithms have been proposed. Recently, relevance propagation methods as one of the salient combinational algorithms, has attracted many information retrieval (IR) researchers' attention. In these methods the content-based attributes are propagated from one page to another through web graph. In this paper, we propose a generic method for exploiting the estimated popularity degree of pages (such as their PageRank score) to improve the propagation process. Experimental results based on TREC 2003 and 2004 gathered in Microsoft LETOR 3.0 benchmark collection show that this idea can improve the precision of the corresponding models without any additional online complexity.
relevance propagation ranking information retrieval popularity measure