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).

翻译：搜索引擎中的查询处理可以针对所有查询进行优化。为此，系统组件参数（如加权函数或自动查询扩展模型）可以基于历史查询进行优化或学习。然而，更值得关注的是通过调整组件参数，以逐查询为基础优化处理流程——这正是选择性查询处理所实现的功能。选择性查询处理在查询执行时从候选处理流程中选择一个，该选择基于查询特征。本文从效果与效率两个维度考察了不同场景下的选择性查询处理，包括选择性查询扩展及其他形式的处理（例如，当词项加权函数变化或扩展模型变化时）。研究发现，当使用最优训练处理流程及其对应的查询扩展流程时，效果与效率的权衡达到最佳。这一结论对实际搜索引擎而言也最为自然，因为这两个流程共享相同的核心引擎（如同种词项加权函数）。