并行学习排序用于信息检索

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI:10.1145/2009916.2010060

Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw

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引用次数: 9

摘要

排序学习是信息检索中一种有效的排序方法。虽然现有的研究主要关注的是准确性，但由于大规模训练数据的空前可用性和对排名函数不断更新的需求，学习效率也成为一个重要的问题。在本文中，我们研究并行学习排序，目标是同时提高准确性和效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Parallel learning to rank for information retrieval

Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.

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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

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