Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploring the similarity space
ACM SIGIR Forum
Personalization of search engine services for effective retrieval and knowledge management
ICIS '00 Proceedings of the twenty first international conference on Information systems
Machine Learning
Modern Information Retrieval
A new family of online algorithms for category ranking
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
IEEE Transactions on Knowledge and Data Engineering
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Linear discriminant model for information retrieval
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
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
Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search
Journal of Management Information Systems
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Calibrated lazy associative classification
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
On-Demand Associative Cross-Language Information Retrieval
SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
OrdRank: Learning to Rank with Ordered Multiple Hyperplanes
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning to rank with a novel kernel perceptron method
Proceedings of the 18th ACM conference on Information and knowledge management
Improving Text Rankers by Term Locality Contexts
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
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
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Semi-supervised ranking for document retrieval
Computer Speech and Language
Calibrated lazy associative classification
Information Sciences: an International Journal
Transductive learning to rank using association rules
Expert Systems with Applications: An International Journal
Associative tag recommendation exploiting multiple textual features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rule-based active sampling for learning to rank
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Cost-effective on-demand associative author name disambiguation
Information Processing and Management: an International Journal
Query-biased learning to rank for real-time twitter search
Proceedings of the 21st ACM international conference on Information and knowledge management
Improving on-demand learning to rank through parallelism
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
A survey of learning to rank for real-time twitter search
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
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Some applications have to present their results in the form of ranked lists. This is the case of many information retrieval applications, in which documents must be sorted according to their relevance to a given query. This has led the interest of the information retrieval community in methods that automatically learn effective ranking functions. In this paper we propose a novel method which uncovers patterns (or rules) in the training data associating features of the document with its relevance to the query, and then uses the discovered rules to rank documents. To address typical problems that are inherent to the utilization of association rules (such as missing rules and rule explosion), the proposed method generates rules on a demand-driven basis, at query-time. The result is an extremely fast and effective ranking method. We conducted a systematic evaluation of the proposed method using the LETOR benchmark collections. We show that generating rules on a demand-driven basis can boost ranking performance, providing gains ranging from 12% to 123%, outperforming the state-of-the-art methods that learn to rank, with no need of time-consuming and laborious pre-processing. As a highlight, we also show that additional information, such as query terms, can make the generated rules more discriminative, further improving ranking performance.