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
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Information Retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Scaling up all pairs similarity search
Proceedings of the 16th international conference on World Wide Web
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Regularizing query-based retrieval scores
Information Retrieval
A boosting algorithm for learning bipartite ranking functions with partially labeled data
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
Semi-supervised document retrieval
Information Processing and Management: an International Journal
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Here or there: preference judgments for relevance
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
LambdaMerge: merging the results of query reformulations
Proceedings of the fourth ACM international conference on Web search and data mining
Semi-supervised ranking aggregation
Information Processing and Management: an International Journal
Relevance feedback exploiting query-specific document manifolds
Proceedings of the 20th ACM international conference on Information and knowledge management
Reusing historical interaction data for faster online learning to rank for IR
Proceedings of the sixth ACM international conference on Web search and data mining
Clustering-based transduction for learning a ranking model with limited human labels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We propose a semi-supervised learning to rank algorithm. It learns from both labeled data (pairwise preferences or absolute labels) and unlabeled data. The data can consist of multiple groups of items (such as queries), some of which may contain only unlabeled items. We introduce a preference regularizer favoring that similar items are similar in preference to each other. The regularizer captures manifold structure in the data, and we also propose a rank-sensitive version designed for top-heavy retrieval metrics including NDCG and mean average precision. The regularizer is employed in SSLambdaRank, a semi-supervised version of LambdaRank. This algorithm directly optimizes popular retrieval metrics and improves retrieval accuracy over LambdaRank, a state-of-the-art ranker that was used as part of the winner of the Yahoo! Learning to Rank challenge 2010. The algorithm runs in linear time in the number of queries, and can work with huge datasets.