Hierarchically Constrained Adaptive Ad Exposure (HCA2E) for Dynamic Feed Advertising

The Hierarchically Constrained Adaptive Ad Exposure (HCA2E) framework treats each user request as a knapsack item and uses a hierarchical greedy‑plus‑beam‑search optimization with a preservation‑order strategy to jointly maximize platform revenue and user experience while respecting global and per‑request ad‑placement constraints, achieving near‑optimal performance and stable, scalable results in extensive offline and online feed‑advertising experiments.

Real-time Controldynamic ad placementfeed recommendation

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Hierarchically Constrained Adaptive Ad Exposure (HCA2E) for Dynamic Feed Advertising

AntTech

Apr 15, 2020 · Artificial Intelligence

Ant Financial Research Highlights at WWW2020: Enhanced‑RCNN, IntentDial, Captcha Solver, Billion‑Scale Knapsack, and EET Loss

The article summarizes five Ant Financial papers accepted at WWW2020, covering an efficient sentence‑similarity model (Enhanced‑RCNN), a graph‑based multi‑turn dialogue system (IntentDial), a low‑label captcha recognizer, a solver for billion‑scale knapsack problems, and a novel equal‑distance/equal‑distribution triplet loss for image retrieval.

Ant FinancialDialogue SystemsWWW2020

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Ant Financial Research Highlights at WWW2020: Enhanced‑RCNN, IntentDial, Captcha Solver, Billion‑Scale Knapsack, and EET Loss

Alibaba Cloud Developer

Dec 5, 2018 · Artificial Intelligence

How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack

This article explains how Alibaba's search team leverages AI techniques such as graph embedding, scenario‑based recommendation, and a multiple‑choice knapsack model to intelligently select complementary items during the Double Eleven shopping festival, balancing price constraints, user experience, and conversion efficiency.

Recommendation Systemse‑commercegraph embedding

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How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack