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
Making large-scale support vector machine learning practical
Advances in kernel methods
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
Quantum computation and quantum information
Quantum computation and quantum information
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A study of relevance propagation for web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Directly optimizing evaluation measures in learning to rank
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A study of learning a merge model for multilingual information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Neural Network Approach for Learning Object Ranking
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
FPGA Acceleration of RankBoost in Web Search Engines
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
BoltzRank: learning to maximize expected ranking gain
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Learning to rank from Bayesian decision inference
Proceedings of the 18th ACM conference on Information and knowledge management
IntervalRank: isotonic regression with listwise and pairwise constraints
Proceedings of the third ACM international conference on Web search and data mining
An immune programming-based ranking function discovery approach for effective information retrieval
Expert Systems with Applications: An International Journal
Cost-sensitive supported vector learning to rank imbalanced data set
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Gradient descent optimization of smoothed information retrieval metrics
Information Retrieval
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
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Semi-supervised ranking for document retrieval
Computer Speech and Language
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
RankDE: learning a ranking function for information retrieval using differential evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning a merge model for multilingual information retrieval
Information Processing and Management: an International Journal
Ranking function adaptation with boosting trees
ACM Transactions on Information Systems (TOIS)
Learning to rank with nonlinear monotonic ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Optimized top-k processing with global page scores on block-max indexes
Proceedings of the fifth ACM international conference on Web search and data mining
Maximum margin ranking algorithms for information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Learning to rank for robust question answering
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to rank duplicate bug reports
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
A multi-stage learning framework for intelligent system
Expert Systems with Applications: An International Journal
Direct optimization of ranking measures for learning to rank models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
Proceedings of the 23rd international conference on World wide web
Learning to Rank with Extreme Learning Machine
Neural Processing Letters
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Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss notonly inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.