On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Hyperlink Analysis for the Web
IEEE Internet Computing
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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 study of the dirichlet priors for term frequency normalisation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th 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
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Aggregation of web search engines based on users' preferences in WebFusion
Knowledge-Based Systems
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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
CiteRank: combination similarity and static ranking with research paper searching
International Journal of Internet Technology and Secured Transactions
A combination ranking model for research paper social bookmarking systems
AMT'11 Proceedings of the 7th international conference on Active media technology
Building a targeted mobile advertising system for location-based services
Decision Support Systems
Applying reinforcement learning for web pages ranking algorithms
Applied Soft Computing
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Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as BM25 and PageRank. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an adaptive method, called A3CRank, based on the content, connectivity, and click-through data triple. Our method tries to aggregate ranking algorithms such as BM25, PageRank, and TF-IDF. We have used reinforcement learning to incorporate user behavior and find a measure of user satisfaction for each ranking algorithm. Furthermore, OWA, an aggregation operator is used for merging the results of the various ranking algorithms. A3CRank adapts itself with user needs and makes use of user clicks to aggregate the results of ranking algorithms. A3CRank is designed to overcome some of the shortcomings of existing ranking algorithms by combining them together and producing an overall better ranking criterion. Experimental results indicate that A3CRank outperforms other combinational ranking algorithms such as Ranking SVM in terms of P@n and NDCG metrics. We have used 130 queries on University of California at Berkeley's web to train and evaluate our method.