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Research Reading: Approaches to click-through rate estimation (3/x) 🤓

Part 3 of the CTR estimation research series: dissecting Alibaba's AAAI 2019 Deep Interest Evolution Network (DIEN) for modeling temporal user interest shifts.

CTR Prediction Ads Ranking DIEN Recommender Systems

Previously I shared two research readings (Part 1 and Part 2) for CTR estimation and keeping up with the spirit of this series, I am sharing an overview of a very interesting research paper Deep Interest Evolution Network for Click-Through Rate Prediction authored by the Alibaba Group and published in AAAI Conference on Artificial Intelligence 2019.

Illustration 1 — Research Reading: Approaches to click-through rate estimation 3xIllustration 2 — Research Reading: Approaches to click-through rate estimation 3xIllustration 3 — Research Reading: Approaches to click-through rate estimation 3xIllustration 4 — Research Reading: Approaches to click-through rate estimation 3xIllustration 5 — Research Reading: Approaches to click-through rate estimation 3xIllustration 6 — Research Reading: Approaches to click-through rate estimation 3xIllustration 7 — Research Reading: Approaches to click-through rate estimation 3xIllustration 8 — Research Reading: Approaches to click-through rate estimation 3xIllustration 9 — Research Reading: Approaches to click-through rate estimation 3xIllustration 10 — Research Reading: Approaches to click-through rate estimation 3xIllustration 11 — Research Reading: Approaches to click-through rate estimation 3xIllustration 12 — Research Reading: Approaches to click-through rate estimation 3xIllustration 13 — Research Reading: Approaches to click-through rate estimation 3xIllustration 14 — Research Reading: Approaches to click-through rate estimation 3xIllustration 15 — Research Reading: Approaches to click-through rate estimation 3xIllustration 16 — Research Reading: Approaches to click-through rate estimation 3xIllustration 17 — Research Reading: Approaches to click-through rate estimation 3xIllustration 18 — Research Reading: Approaches to click-through rate estimation 3xIllustration 19 — Research Reading: Approaches to click-through rate estimation 3xIllustration 20 — Research Reading: Approaches to click-through rate estimation 3xIllustration 21 — Research Reading: Approaches to click-through rate estimation 3xIllustration 22 — Research Reading: Approaches to click-through rate estimation 3xIllustration 23 — Research Reading: Approaches to click-through rate estimation 3xIllustration 24 — Research Reading: Approaches to click-through rate estimation 3xIllustration 25 — Research Reading: Approaches to click-through rate estimation 3xIllustration 26 — Research Reading: Approaches to click-through rate estimation 3xIllustration 27 — Research Reading: Approaches to click-through rate estimation 3xIllustration 28 — Research Reading: Approaches to click-through rate estimation 3xIllustration 29 — Research Reading: Approaches to click-through rate estimation 3xIllustration 30 — Research Reading: Approaches to click-through rate estimation 3x

Illustration 31 — Research Reading: Approaches to click-through rate estimation 3x

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Hyper-Personalized Ad Campaigns using Generative AI + Quick Demo using Google Gemini Pro Vision 🎯

Bridging production ad-ranking personalization with multimodal generative synthesis using Gemini Pro Vision.

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Dec 2019
Research Reading: Approaches to click-through rate estimation (2/x) 🤓

Part 2 of the CTR estimation research series: examining Yahoo! Research's Deeply Supervised Semantic Model for sponsored search click prediction.

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Dec 2019
Research Reading: Approaches to click-through rate estimation (1/x) 🤓

Part 1 of the CTR estimation research series: analyzing Microsoft Bing's production model-ensemble architectures for large-scale search ad click prediction.

Personalization & Ranking