重影:大规模零售搜索中上下文化的内联查询完成

Lakshmi Ramachandran, Uma Murthy
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引用次数: 1

摘要

查询自动完成提供了一个排序的查询列表,作为用户输入的前缀的建议。重影是通过在搜索框内突出显示建议的文本来自动完成搜索推荐的过程。我们建议使用基于行为的推荐模型以及客户搜索上下文来对高置信度查询进行ghost。我们在一个零售产品系统上测试了鬼影,在超过1.4亿个搜索会话上。我们发现,基于会话上下文的重影显着提高了提供建议的接受度6.18%,减少了搜索中的拼写错误4.42%,并提高了净销售额0.14%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ghosting: contextualized inline query completion in large scale retail search
Query auto-completion presents a ranked list of queries as suggestions for a user-entered prefix. Ghosting is the process of auto-completing a search recommendation by highlighting the suggested text inline within the search box. We propose the use of a behavior-based recommendation model along with customer search context to ghost on high-confidence queries. We tested ghosting on a retail production system, on over 140 million search sessions. We found that session-context based ghosting significantly increased the acceptance of offered suggestions by 6.18%, reduced misspellings among searches by 4.42%, and improved net sales by 0.14%.
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