Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Approximating the top-m passages in a parallel question answering system
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Using graphics processors for high-performance IR query processing
Proceedings of the 17th international conference on World Wide Web
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
RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Tree Projection-Based Frequent Itemset Mining on Multicore CPUs and GPUs
SBAC-PAD '10 Proceedings of the 2010 22nd International Symposium on Computer Architecture and High Performance Computing
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Parallel learning to rank for information retrieval
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
A Fast Algorithm for Constructing Inverted Files on Heterogeneous Platforms
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
GPApriori: GPU-Accelerated Frequent Itemset Mining
CLUSTER '11 Proceedings of the 2011 IEEE International Conference on Cluster Computing
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Traditional Learning to Rank (L2R) is usually conducted in a batch mode in which a single ranking function is learned to order results for future queries. This approach is not flexible since future queries may differ considerably from those present in the training set and, consequently, the learned function may not work properly. Ideally, a distinct learning function should be learned on demand for each query. Nevertheless, on-demand L2R may significantly degrade the query processing time, as the ranking function has to be learned on-the-fly before it can be applied. In this paper we present a parallel implementation of an on-demand L2R technique that reduces drastically the response time of previous serial implementation. Our implementation makes use of thousands of threads of a GPU to learn a ranking function for each query, and takes advantage of a reduced training set obtained through active learning. Experiments with the LETOR benchmark show that our proposed approach achieves a mean speedup of 127x in query processing time when compared to the sequential version, while producing very competitive ranking effectiveness.