Semantic In-Domain Product Identification for Search Queries
This work addresses the challenge of enhancing user experiences for companies like Adobe with many products, though it appears incremental as it builds on existing product classification methods.
The paper tackled the problem of accurately identifying products in search queries using a semantic model trained on user behavioral data, resulting in a >25% relative improvement in CTR, a >50% decrease in null rate, and a 2x increase in app cards surfaced.
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.