Semantic In-Domain Product Identification for Search Queries
<|reference_start|>Semantic In-Domain Product Identification for Search Queries: 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.<|reference_end|>
@article{sharma2024semantic,
    title={Semantic In-Domain Product Identification for Search Queries},
    author={Sanat Sharma, Jayant Kumar, Twisha Naik, Zhaoyu Lu, Arvind Srikantan,
  Tracy Holloway King},
    journal={arXiv preprint arXiv:2404.09091},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09091},
    primaryClass={cs.IR cs.AI cs.CL cs.LG}
}