Ranking under temporal constraints

Lidan Wang, Donald Metzler, Jimmy J. Lin
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引用次数: 39

Abstract

This paper introduces the notion of temporally constrained ranked retrieval, which, given a query and a time constraint, produces the best possible ranked list within the specified time limit. Naturally, more time should translate into better results, but the ranking algorithm should always produce some results. This property is desirable from a number of perspectives: to cope with diverse users and information needs, as well as to better manage system load and variance in query execution times. We propose two temporally constrained ranking algorithms based on a class of probabilistic prediction models that can naturally incorporate efficiency constraints: one that makes independent feature selection decisions, and the other that makes joint feature selection decisions. Experiments on three different test collections show that both ranking algorithms are able to satisfy imposed time constraints, although the joint model outperforms the independent model in being able to deliver more effective results, especially under tight time constraints, due to its ability to capture feature dependencies.
时间约束下的排序
本文引入了时间约束排序检索的概念,在给定查询和时间约束的情况下,在指定的时间限制内生成可能的最佳排序列表。当然,更多的时间应该转化为更好的结果,但排名算法应该总是产生一些结果。从许多角度来看,这个属性都是可取的:处理不同的用户和信息需求,以及更好地管理系统负载和查询执行时间的变化。我们提出了两种基于一类自然包含效率约束的概率预测模型的时间约束排序算法:一种是独立的特征选择决策,另一种是联合的特征选择决策。在三个不同的测试集合上的实验表明,两种排序算法都能够满足强加的时间约束,尽管联合模型在能够提供更有效的结果方面优于独立模型,特别是在紧迫的时间约束下,因为它能够捕获特征依赖关系。
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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