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LinkedIn deploys CADET transformer for 11% ad CTR lift

By PulseAugur Editorial · [1 sources] ·

LinkedIn has developed and deployed CADET, a decoder-only transformer model for predicting ad click-through rates (CTR). This new model significantly outperforms their previous LiRank baseline, achieving an 11.04% CTR lift in A/B testing. CADET incorporates several innovations, including a context-conditioned decoding architecture, a self-gated attention mechanism, and timestamp-based positional embeddings, all engineered for efficient industrial-scale deployment. AI

IMPACT This deployment demonstrates the effectiveness of transformer architectures in specialized domains like ad CTR prediction, potentially influencing future recommender system designs.

RANK_REASON Deployment of a new model within an existing product by a major tech company.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LinkedIn deploys CADET transformer for 11% ad CTR lift

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Tool
Deployment of a new model within an existing product by a major tech company.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, model release, infra
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High
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45 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · David Pardoe, Neil Daftary, Miro Furtado, Aditya Aiyer, Yu Wang, Liuqing Li, Tao Song, Lars Hertel, Young Jin Yun, Senthil Radhakrishnan, Zhiwei Wang, Tommy Li, Khai Tran, Ananth Nagarajan, Ali Naqvi, Yue Zhang, Renpeng Fang, Avi Romascanu, Arjun Kulothu… ·

    CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer

    arXiv:2602.11410v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction is fundamental to online advertising systems. While Deep Learning Recommendation Models (DLRMs) with explicit feature interactions have long dominated this domain, recent advances in generativ…

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