Searching, fast and slow, through product catalogs
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866907754957897728 |
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| author | Ubrangala, Dayananda Sharma, Juhi Rangappa, Sharath Kumar R, Kiran Kondapalli, Ravi Prasad Boué, Laurent |
| author_facet | Ubrangala, Dayananda Sharma, Juhi Rangappa, Sharath Kumar R, Kiran Kondapalli, Ravi Prasad Boué, Laurent |
| contents | String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In this paper, we present a unified architecture for SKU search that provides both a real-time suggestion system (based on a Trie data structure) as well as a lower latency search system (making use of character level TF-IDF in combination with language model vector embeddings) where users initiate the search process explicitly. We carry out ablation studies that justify designing a complex search system composed of multiple components to address the delicate trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as an example, we show how our system vastly outperforms, in all aspects, the results provided by the default search engine. Finally, we show how SKU descriptions may be enhanced via generative text models (using gpt-3.5-turbo) so that the consumers of the search results may get more context and a generally better experience when presented with the results of their SKU search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00737 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Searching, fast and slow, through product catalogs Ubrangala, Dayananda Sharma, Juhi Rangappa, Sharath Kumar R, Kiran Kondapalli, Ravi Prasad Boué, Laurent Information Retrieval Artificial Intelligence Machine Learning Software Engineering String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In this paper, we present a unified architecture for SKU search that provides both a real-time suggestion system (based on a Trie data structure) as well as a lower latency search system (making use of character level TF-IDF in combination with language model vector embeddings) where users initiate the search process explicitly. We carry out ablation studies that justify designing a complex search system composed of multiple components to address the delicate trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as an example, we show how our system vastly outperforms, in all aspects, the results provided by the default search engine. Finally, we show how SKU descriptions may be enhanced via generative text models (using gpt-3.5-turbo) so that the consumers of the search results may get more context and a generally better experience when presented with the results of their SKU search. |
| title | Searching, fast and slow, through product catalogs |
| topic | Information Retrieval Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2401.00737 |