Boosting a weak learning algorithm by majority
Information and Computation
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
Evaluation by highly relevant documents
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Evaluating high accuracy retrieval techniques
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking and Reranking with Perceptron
Machine Learning
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
University of chicago at CLEF2004: cross-language text and spoken document retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Distributed web search efficiency by truncating results
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Linear feature-based models for information retrieval
Information Retrieval
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Fast learning of document ranking functions with the committee perceptron
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Retrieval sensitivity under training using different measures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
CMIC at INEX 2007: Book Search Track
Focused Access to XML Documents
Book search: indexing the valuable parts
Proceedings of the 2008 ACM workshop on Research advances in large digital book repositories
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Robust query-specific pseudo feedback document selection for query expansion
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Achieving high accuracy retrieval using intra-document term ranking
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
A cascade ranking model for efficient ranked retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning to rank for robust question answering
Proceedings of the 21st ACM international conference on Information and knowledge management
Fast candidate generation for real-time tweet search with bloom filter chains
ACM Transactions on Information Systems (TOIS)
Document vector representations for feature extraction in multi-stage document ranking
Information Retrieval
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High precision at the top ranks has become a new focus of research in information retrieval. This paper presents the multiple nested ranker approach that improves the accuracy at the top ranks by iteratively re-ranking the top scoring documents. At each iteration, this approach uses the RankNet learning algorithm to re-rank a subset of the results. This splits the problem into smaller and easier tasks and generates a new distribution of the results to be learned by the algorithm. We evaluate this approach using different settings on a data set labeled with several degrees of relevance. We use the normalized discounted cumulative gain (NDCG) to measure the performance because it depends not only on the position but also on the relevance score of the document in the ranked list. Our experiments show that making the learning algorithm concentrate on the top scoring results improves precision at the top ten documents in terms of the NDCG score.