选择性查询处理中的有效性和效率权衡
Effectiveness and Efficiency Trade-off in Selective Query Processing
摘要：
可以优化搜索引擎中的查询处理以用于所有查询。为此，可以优化系统组件参数，例如加权函数或自动查询扩展模型，或者从过去的查询中学习。然而，通过调整组件参数来逐个查询地优化处理线程可能更有意思;这就是选择性查询处理所做的。选择性查询处理使用在查询时选择的候选处理线程之一。选择基于查询功能。在本文中，我们研究了不同设置中的选择性查询处理，包括有效性和效率;这包括选择性查询扩展和其他形式的选择性查询处理（例如，当项加权函数变化时或者当扩展模型变化时）。我们发现，当使用最佳训练的处理线程及其查询扩展计数器部分时，可以获得有效性和效率之间的最佳权衡。这对于实字引擎来说似乎也是最自然的，因为两个线程使用相同的核心引擎（例如，相同项加权函数）。
英文摘要：
Query processing in search engines can be optimized for use for all queries. For this, system component parameters such as the weighting function or the automatic query expansion model can be optimized or learned from past queries. However, it may be more interesting to optimize the processing thread on a query-by-query basis by adjusting the component parameters; this is what selective query processing does. Selective query processing uses one of the candidate processing threads chosen at query time. The choice is based on query features. In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e.g., when the term weighting function varies or when the expansion model varies). We found that the best trade-off between effectiveness and efficiency is obtained when using the best trained processing thread and its query expansion counter part. This seems to be also the most natural for a real-word engine since the two threads use the same core engine (e.g., same term weighting function).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
