LiDDA: Data Driven Attribution at LinkedIn 文章

ArXiv CS.AI2026-05-28NEWSen作者: John Bencina, Erkut Aykutlug, Yue Chen, Zerui Zhang, Stephanie Sorenson, Shao Tang, Changshuai Wei

摘要

arXiv:2505.09861v3 Announce Type: replace-cross Abstract: Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence and vital to any marketing business and advertising platform. In this paper, we introduce a unified transformer-based attribution approach that can handle member-level data, aggregate-level data, and integration of external macro factors. We detail the large scale implementation of the approach at LinkedIn, showcasing significant impact. We also share learnings and insights which are broadly applicable to the marketing and ad tech fields.

相关事件查看全部 (1)

LiDDA: Data Driven Attribution at LinkedIn
2026-05-28PRODUCT_LAUNCH影响: MEDIUM

相关人物

暂无数据

相关产品

暂无数据