Retrieval sensitivity under training using different measures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Are click-through data adequate for learning web search rankings?
Proceedings of the 17th ACM conference on Information and knowledge management
Usefulness of quality click-through data for training
Proceedings of the 2009 workshop on Web Search Click Data
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Bayesian inference for Plackett-Luce ranking models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An improved markov random field model for supporting verbose queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
PQC: personalized query classification
Proceedings of the 18th ACM conference on Information and knowledge management
A Boosting Approach for Learning to Rank Using SVD with Partially Labeled Data
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
Large-scale bot detection for search engines
Proceedings of the 19th international conference on World wide web
An immune programming-based ranking function discovery approach for effective information retrieval
Expert Systems with Applications: An International Journal
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Tie-breaking bias: effect of an uncontrolled parameter on information retrieval evaluation
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
Finding related sentence pairs in MEDLINE
Information Retrieval
Feature selection under learning to rank model for multimedia retrieve
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
An FPGA-based accelerator for LambdaRank in Web search engines
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Reliability and effectiveness of clickthrough data for automatic image annotation
Multimedia Tools and Applications
Learning to temporally order medical events in clinical text
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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The task of "learning to rank" has emerged as an active and growing area of research both in information retrieval and machine learning. The goal is to design and apply methods to automatically learn a function from training data, such that the function can sort objects (e.g., documents) according to their degrees of relevance, preference, or importance as defined in a specific application.