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Sep 3, 2025 · Backend Development

How a DAG‑Based Word Distribution Platform Boosts E‑Commerce Search Efficiency

This article explains the background, supported scenarios, overall and evolving architecture, script hot‑deployment, CI/CD workflow, and future plans of a DAG‑driven word distribution platform that unifies keyword recommendation across dozens of e‑commerce use cases, improving flexibility, development cost, and user experience.

DAG architectureci/cde-commerce backend

0 likes · 17 min read

How a DAG‑Based Word Distribution Platform Boosts E‑Commerce Search Efficiency

NewBeeNLP

Aug 5, 2024 · Industry Insights

How Alibaba Cloud Scales Search Recommendations with Big Data, AI, and LLMs

This article details Alibaba Cloud's end‑to‑end architecture for search and advertising recommendation, covering the data platform, AI services, feature‑store design, training and inference optimizations, and the integration of large language models for new recommendation scenarios.

AI PlatformAlibaba CloudBig Data

0 likes · 17 min read

How Alibaba Cloud Scales Search Recommendations with Big Data, AI, and LLMs

Meituan Technology Team

Mar 28, 2024 · Artificial Intelligence

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

The paper describes how Meituan’s food‑delivery search advertising uses a heterogeneous billion‑node graph and the GraphET engine to boost weak‑supply recall, detailing a progression from fine‑grained modeling to GPT‑enhanced pre‑training, and presenting a scalable training and low‑latency inference architecture that handles hundreds of billions of edges.

GraphETLarge-Scale GraphMeituan

0 likes · 27 min read

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

Meituan Technology Team

Jul 13, 2023 · Artificial Intelligence

Intelligent Companion Search Guidance for Meituan Waimai: Challenges, Solutions, and Future Directions

Meituan’s delivery team created an intelligent, real‑time companion‑type search guidance system for its waimai platform—combining a smart in‑box word refresh triggered by edge‑intelligence intent signals and a unified multi‑scenario query‑recommendation model with self‑supervised pre‑training and multi‑objective optimization—delivering over 1 % DAU growth, 1.7 % UV_RPM increase, and up to 187 % CTR lifts while outlining future extensions to more pages and large‑model embeddings.

Meituanfood deliveryintelligent companion

0 likes · 31 min read

Intelligent Companion Search Guidance for Meituan Waimai: Challenges, Solutions, and Future Directions

DataFunSummit

Jun 8, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

This article presents a comprehensive overview of search term recommendation in QQ Browser, covering various recommendation scenarios, challenges, query library architecture, multi‑task ranking models, coarse‑to‑fine ranking pipelines, auto‑completion strategies, and future research directions.

AIRecommendation Systemsmachine learning

0 likes · 14 min read

Search Term Recommendation: Scenarios, Algorithm Design, and Future Directions

DataFunTalk

May 15, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions

This article presents an in‑depth overview of search term recommendation in QQ Browser, covering the various recommendation scenarios, the composition of recommendation items, the multi‑stage algorithm architecture, key technical challenges, evaluation metrics, and future research directions such as multi‑task and session‑aware modeling.

future researchmachine learningmulti-task learning

0 likes · 15 min read

Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions

DataFunTalk

Oct 1, 2020 · Artificial Intelligence

Building and Applying a Vector System for Search and Recommendation at NetEase Yanxuan

This article describes how NetEase Yanxuan has designed, trained, and deployed a unified vector representation system to power various e‑commerce search and recommendation scenarios, covering model choices, incremental learning strategies, large‑scale similarity computation, and practical lessons from real‑world deployments.

e‑commercelarge-scale similaritymachine learning

0 likes · 18 min read

Building and Applying a Vector System for Search and Recommendation at NetEase Yanxuan

Alibaba Cloud Developer

Nov 14, 2016 · Artificial Intelligence

How Alibaba Boosted Double 11 Sales with Deep Reinforcement Learning

Alibaba’s Double 11 event shattered sales records by leveraging deep reinforcement learning and adaptive online learning in its search and recommendation systems, which increased click‑through rates by 10‑20% and dramatically improved the real‑time shopping experience for hundreds of millions of users.

AI in e-commerceAlibabaadaptive online learning

0 likes · 4 min read

How Alibaba Boosted Double 11 Sales with Deep Reinforcement Learning