精简列表关联模型

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI:10.1145/2808194.2809491

Fernando Diaz

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引用次数: 21

摘要

传统上，伪相关反馈被实现为从目标语料库中重新检索文档的昂贵方法。在这项工作中，我们证明了，对于高精度指标，重新排序原始反馈集提供了几乎相同的性能，以显着降低延迟的重新检索。

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Condensed List Relevance Models

Pseudo-relevance feedback has traditionally been implemented as an expensive re-retrieval of documents from the target corpus. In this work, we demonstrate that, for high precision metrics, re-ranking the original feedback set provides nearly identical performance to re-retrieval with significantly lower latency.

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Proceedings of the 2015 International Conference on The Theory of Information Retrieval

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