Pith. sign in

REVIEW

Semantic In-Domain Product Identification for Search Queries

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.09091 v2 pith:ALKYAYX4 submitted 2024-04-13 cs.IR cs.AIcs.CLcs.LG

Sanat Sharma , Jayant Kumar , Twisha Naik , Zhaoyu Lu , Arvind Srikantan , Tracy Holloway King This is my paper
classification cs.IRcs.AIcs.CLcs.LG
keywords productqueriesacrossidentificationratesearchsemanticuser
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Pith tools