Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Impact of search engines on page popularity
Proceedings of the 13th international conference on World Wide Web
Average-clicks: a new measure of distance on the World Wide Web
Journal of Intelligent Information Systems - Special issue on web intelligence
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A study of relevance propagation for web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic relevance propagation model for hypertext retrieval
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Hits on the web: how does it compare?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
DistanceRank: An intelligent ranking algorithm for web pages
Information Processing and Management: an International Journal
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Slash-based relevance propagation model for topic distillation
Journal of Web Engineering
| Hi-index | 0.00 |
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.