Author

Alimama Tech

Official Alimama tech channel, showcasing all of Alimama's technical innovations.

255

Articles

Likes

1.9k

Views

Comments

Latest from Alimama Tech

100 recent articles max

Alimama Tech

Aug 16, 2024 · Artificial Intelligence

SPLAM: Sub‑Path Linear Approximation for Accelerating Diffusion Model Sampling

SPLAM (Sub‑Path Linear Approximation Model) accelerates diffusion‑model image synthesis by linearly approximating short sub‑paths of the probability‑flow ODE, allowing high‑quality generation in as few as four steps, outperforming prior fast‑sampling methods on COCO benchmarks and being deployed in Alibaba Mama’s recommendation system.

AI Image GenerationSPLAMdiffusion models

0 likes · 11 min read

SPLAM: Sub‑Path Linear Approximation for Accelerating Diffusion Model Sampling

Alimama Tech

Aug 2, 2024 · Artificial Intelligence

Multimodal Representations Boost Taobao Display Advertising CTR

Alibaba’s advertising team introduces semantic‑aware contrastive learning to pre‑train multimodal image‑text embeddings, integrates them via SimTier and MAKE into ID‑based CTR models, achieving up to 6.9% lift in Taobao display ad click‑through rates and improving long‑tail item performance.

CTR predictionContrastive LearningE-commerce

0 likes · 21 min read

Multimodal Representations Boost Taobao Display Advertising CTR

Alimama Tech

Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

Generative ModelsMarketing AIOnline Advertising

0 likes · 18 min read

Generative Auto-bidding via Diffusion Modeling (AIGB)

Alimama Tech

Jul 15, 2024 · Artificial Intelligence

Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge

The article explains how NeurIPS ranks among top AI conferences, introduces the newly selected “Auto‑Bidding in Large‑Scale Auctions” competition, outlines its technical background, four generations of bidding strategies—from classic control to generative models—and details the competition’s tracks, rewards, and how researchers can participate.

AdvertisingCompetitionGenerative AI

0 likes · 12 min read

Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge

Alimama Tech

Jul 11, 2024 · Artificial Intelligence

Efficient Local Search for Guaranteed Display Advertising Inventory Allocation with Multilinear Constraints

The paper introduces LS‑IMP, a two‑stage local‑search algorithm with four novel operators that efficiently solves guaranteed‑delivery advertising inventory allocation under non‑convex multilinear media‑preference constraints, consistently outperforming commercial solvers and heuristics in solution quality and speed on real‑world datasets.

Online Advertisingalgorithminventory allocation

0 likes · 17 min read

Efficient Local Search for Guaranteed Display Advertising Inventory Allocation with Multilinear Constraints

Alimama Tech

Jul 2, 2024 · Artificial Intelligence

NeurIPS 2024 Competition: Auto-Bidding in Large-Scale Auctions

The 2024 NeurIPS Competition, organized by Peking University's PAAI lab and Alibaba‑Mama, challenges teams to build auto‑bidding agents—using generative models for the AIGB Track or handling uncertainty for the General Track—to maximize ad performance in large‑scale auctions, with registration open until August 8 and prize pools up to $6,000 per track.

CompetitionNeurIPSauction

0 likes · 4 min read

NeurIPS 2024 Competition: Auto-Bidding in Large-Scale Auctions

Alimama Tech

Jun 21, 2024 · Artificial Intelligence

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

CausalMMM introduces an encoder‑decoder framework that automatically discovers heterogeneous, interpretable causal graphs among advertising channels while modeling temporal decay and saturation, using Granger‑based variational inference, and achieves over 5.7% improvement in causal structure learning and significant GMV prediction gains on Alibaba’s data.

Time Series Forecastingmarketing mix modelingvariational inference

0 likes · 16 min read

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

Alimama Tech

Jun 13, 2024 · Artificial Intelligence

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction

The article describes Alibaba's approach to distilling privileged features for CTR prediction using a calibration-compatible listwise distillation loss (CLID) that normalizes teacher and student outputs within sessions to align top‑ranking probabilities, improving both accuracy and ranking while preserving calibration.

AI in advertisingCTR predictionListwise distillation

0 likes · 14 min read

Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction

Alimama Tech

May 29, 2024 · Artificial Intelligence

Alibaba Mama Team Papers Accepted at KDD 2024

Alibaba’s Mama technical team secured four paper acceptances at the prestigious KDD 2024 conference in Barcelona, presenting advances such as a diffusion‑based generative bidding model, truthful combinatorial bandit mechanisms for two‑stage ad auctions, bi‑objective contract allocation for guaranteed delivery advertising, and a fast local‑search algorithm for complex contract constraints.

AIAdvertisingBandit

0 likes · 8 min read

Alibaba Mama Team Papers Accepted at KDD 2024

Alimama Tech

May 29, 2024 · Artificial Intelligence

Mixture of Multi‑Modal Experts for Advertising Recall

The Mixed‑Modal Expert Model combines ID features with image and text embeddings through optimized representations and conditional output fusion, dramatically improving advertising recall—especially for long‑tail items—and delivering measurable gains in click‑recall, revenue, CTR, and page views in large‑scale online tests.

Machine LearningModelRecall

0 likes · 15 min read

Mixture of Multi‑Modal Experts for Advertising Recall