Learning to efficiently rank
Learning to efficiently rank
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DOI：
10.1145/1835449.1835475
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发表时间：
2010-07
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影响因子：
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通讯作者：
Lidan Wang
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文献类型：
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作者：
Lidan Wang
已经表明，学习排名方法能够学习高效的排名函数。然而，这些方法大多忽视了效率这一重要问题。考虑到效率和有效性对于真实的搜索引擎都很重要，针对有效性进行优化的模型可能无法满足在生产环境中部署所需的严格效率要求。在这项工作中，我们提出了一个统一的框架，共同优化的效果和效率。我们提出了新的指标，捕捉这两个竞争力量之间的权衡，并设计了一个策略，自动学习模型，直接优化权衡指标。实验表明，以这种方式学习的模型提供了检索效果和效率之间的良好平衡。通过特定的损失函数，学习的模型收敛到熟悉的现有模型，这证明了我们框架的通用性。最后，我们表明，我们的方法自然会导致减少查询执行时间的方差，这是很重要的查询负载平衡和用户满意度。
It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency requirements necessary to deploy in a production environment. In this work, we present a unified framework for jointly optimizing effectiveness and efficiency. We propose new metrics that capture the tradeoff between these two competing forces and devise a strategy for automatically learning models that directly optimize the tradeoff metrics. Experiments indicate that models learned in this way provide a good balance between retrieval effectiveness and efficiency. With specific loss functions, learned models converge to familiar existing ones, which demonstrates the generality of our framework. Finally, we show that our approach naturally leads to a reduction in the variance of query execution times, which is important for query load balancing and user satisfaction.