Semantic In-Domain Product Identification for Search Queries
Sanat Sharma Jayant Kumar Twisha Naik Zhaoyu Lu Arvind Srikantan Tracy Holloway King
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.