Multi-objective optimization in learning to rank

Na Dai, Milad Shokouhi, Brian D. Davison
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引用次数: 7

Abstract

Supervised learning to rank algorithms typically optimize for high relevance and ignore other facets of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly incorporate multiple criteria into quantifying ranking risks. However, these hybrid scores are usually generated based on heuristics without considering potential correlations between individual facets (e.g., freshness versus relevance). In this poster, we empirically demonstrate that the correlation between objective facets in multi-criteria ranking optimization may significantly influence the effectiveness of trained rankers with respect to each objective.
排序学习中的多目标优化
监督学习排序算法通常针对高相关性进行优化,而忽略了搜索质量的其他方面,如新鲜度和多样性。先前关于多目标排名训练的排名者的工作侧重于使用混合标签,结合文档的整体质量,并隐含地将多个标准纳入量化排名风险。然而,这些混合分数通常是基于启发式生成的,而没有考虑各个方面之间的潜在相关性(例如,新鲜度与相关性)。在这张海报中,我们通过经验证明,在多标准排名优化中,客观方面之间的相关性可能会显著影响训练有素的排名者相对于每个目标的有效性。
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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