Recency ranking by diversification of result set

Andrey Styskin, Fedor Romanenko, F. Vorobyev, P. Serdyukov
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引用次数: 20

Abstract

In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content. We propose to solve the recency ranking problem by using result diversification principles and deal with the query's non-topical ambiguity appearing when the need in recent content can be detected only with uncertainty. Our offine and online experiments with millions of queries from real search engine users demonstrate the significant increase in satisfaction of users presented with a search result generated by our approach.
基于结果集多样化的近期排序
在本文中,我们提出了一种自动检测最近敏感查询的web搜索检索方法,并以与最近内容中需要的概率成比例的程度增加普通文档的新鲜度排名。我们提出利用结果多样化原则来解决最近排序问题,并处理仅通过不确定性就可以检测到最近内容中的需求时出现的查询的非主题歧义。我们对来自真实搜索引擎用户的数百万查询进行的离线和在线实验表明,通过我们的方法生成的搜索结果显著提高了用户的满意度。
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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