Exploring Query Understanding for Amazon Product Search
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910555233583104 |
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| author | Luo, Chen Tang, Xianfeng Lu, Hanqing Xie, Yaochen Liu, Hui Dai, Zhenwei Cui, Limeng Joshi, Ashutosh Nag, Sreyashi Li, Yang Li, Zhen Goutam, Rahul Tang, Jiliang Zhang, Haiyang He, Qi |
| author_facet | Luo, Chen Tang, Xianfeng Lu, Hanqing Xie, Yaochen Liu, Hui Dai, Zhenwei Cui, Limeng Joshi, Ashutosh Nag, Sreyashi Li, Yang Li, Zhen Goutam, Rahul Tang, Jiliang Zhang, Haiyang He, Qi |
| contents | Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Query Understanding for Amazon Product Search Luo, Chen Tang, Xianfeng Lu, Hanqing Xie, Yaochen Liu, Hui Dai, Zhenwei Cui, Limeng Joshi, Ashutosh Nag, Sreyashi Li, Yang Li, Zhen Goutam, Rahul Tang, Jiliang Zhang, Haiyang He, Qi Information Retrieval Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search. |
| title | Exploring Query Understanding for Amazon Product Search |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2408.02215 |