基于块级搜索推荐优化的混合目标指向解码器训练

Harsh Kohli
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引用次数: 1

摘要

搜索引擎中与网页查询相关或理想的后续建议通常基于几个不同的参数进行优化——与原始查询的相关性、多样性、点击概率等。可以训练一个或多个排名员,根据这些因素对候选池中的每个建议进行评分。这些评分器通常是成对分类任务,其中每个训练示例由用户查询和候选列表中的单个建议组成。我们提出了一个架构,它接受与给定查询关联的所有候选建议,并输出一个建议块。我们讨论了这种体系结构相对于传统方法的好处,并通过混合目标训练进一步加强每个单独的度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation
Related or ideal follow-up suggestions to a web query in search engines are often optimized based on several different parameters -- relevance to the original query, diversity, click probability etc. One or many rankers may be trained to score each suggestion from a candidate pool based on these factors. These scorers are usually pairwise classification tasks where each training example consists of a user query and a single suggestion from the list of candidates. We propose an architecture that takes all candidate suggestions associated with a given query and outputs a suggestion block. We discuss the benefits of such an architecture over traditional approaches and experiment with further enforcing each individual metric through mixed-objective training.
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