Semantic In-Domain Product Identification for Search Queries
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911892348338176 |
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| author | Sharma, Sanat Kumar, Jayant Naik, Twisha Lu, Zhaoyu Srikantan, Arvind King, Tracy Holloway |
| author_facet | Sharma, Sanat Kumar, Jayant Naik, Twisha Lu, Zhaoyu Srikantan, Arvind King, Tracy Holloway |
| contents | Accurate explicit and implicit product identification in search queries is critical for enhancing user experiences, especially at a company like Adobe which has over 50 products and covers queries across hundreds of tools. In this work, we present a novel approach to training a product classifier from user behavioral data. Our semantic model led to >25% relative improvement in CTR (click through rate) across the deployed surfaces; a >50% decrease in null rate; a 2x increase in the app cards surfaced, which helps drive product visibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic In-Domain Product Identification for Search Queries Sharma, Sanat Kumar, Jayant Naik, Twisha Lu, Zhaoyu Srikantan, Arvind King, Tracy Holloway Information Retrieval Artificial Intelligence Computation and Language Machine Learning Accurate explicit and implicit product identification in search queries is critical for enhancing user experiences, especially at a company like Adobe which has over 50 products and covers queries across hundreds of tools. In this work, we present a novel approach to training a product classifier from user behavioral data. Our semantic model led to >25% relative improvement in CTR (click through rate) across the deployed surfaces; a >50% decrease in null rate; a 2x increase in the app cards surfaced, which helps drive product visibility. |
| title | Semantic In-Domain Product Identification for Search Queries |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.09091 |