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arXiv · 2404.09091

Semantic In-Domain Product Identification for Search Queries

Sanat Sharma·Jayant Kumar·Twisha Naik·Zhaoyu Lu·Arvind Srikantan·Tracy Holloway King
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Abstract

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.

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Sanat Sharma, Jayant Kumar, Twisha Naik, Zhaoyu Lu, Arvind Srikantan, Tracy Holloway King. 2024-04-13. Semantic In-Domain Product Identification for Search Queries. https://arxiv.org/abs/2404.09091

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