Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Finding information on the World Wide Web: the retrieval effectiveness of search engines
Information Processing and Management: an International Journal
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hyperlink Analysis for the Web
IEEE Internet Computing
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Just how dense are dense graphs in the real world?: a methodological note
Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
PageRank on semantic networks, with application to word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
DistanceRank: An intelligent ranking algorithm for web pages
Information Processing and Management: an International Journal
Rank-biased precision for measurement of retrieval effectiveness
ACM Transactions on Information Systems (TOIS)
A3CRank: An adaptive ranking method based on connectivity, content and click-through data
Information Processing and Management: an International Journal
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
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Ranking web pages for presenting the most relevant web pages to user's queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent's parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL_Rank, is proposed. In RL_Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a state and value function of state is used to determine the score of that state (page). Reward is corresponded to number of out links from the current page. Rank scores in RL_Rank are computed in a recursive way. Convergence of these scores is proved. In the next step, we introduce a new hybrid approach using combination of BM25 as a content-based algorithm and RL_Rank. Both proposed algorithms are evaluated by well known benchmark datasets and analyzed according to concerning criteria. Experimental results show using RL concepts leads significant improvements in raking algorithms.