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
Information Retrieval
Modern Information Retrieval
Searching XML documents via XML fragments
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An efficient and versatile query engine for TopX search
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Optimisation methods for ranking functions with multiple parameters
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
University of Waterloo at INEX 2008: Adhoc, Book, and Link-the-Wiki Tracks
Advances in Focused Retrieval
Overview of the INEX 2009 ad hoc track
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
Combining strategies for XML retrieval
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
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Many machine learning classification technologies such as boosting, support vector machine or neural networks have been applied to the ranking problem in information retrieval. However, since the purpose of these learning-torank methods is to directly acquire the sorted results based on the features of documents, they are unable to combine and utilize the existing ranking methods proven to be effective such as BM25 and PageRank. To solve this defect, we conducted a study on learning-to-optimize, which is to construct a learning model or method for optimizing the free parameters in ranking functions. This paper proposes a listwise learning-to-optimize process ListOPT and introduces three alternative differentiable query-level loss functions. The experimental results on the XML dataset of Wikipedia English show that these approaches can be successfully applied to tuning the parameters used in an existing highly cited ranking function BM25. Furthermore, we found that the formulas with optimized parameters indeed improve the effectiveness compared with the original ones.