recommendation system

Zhihu Tech Column

Jun 11, 2025 · Artificial Intelligence

How Minute‑Level Time Decay Boosts User Retention Modeling in Recommendation Systems

This article presents a novel minute‑level future‑reward framework with dual‑delay incentives, activity‑based attribution, multi‑task delayed modeling, and sequential streaming training that dramatically improves user retention prediction accuracy and real‑time performance in large‑scale recommendation platforms.

deep learningmulti‑task modelingreal‑time prediction

0 likes · 17 min read

How Minute‑Level Time Decay Boosts User Retention Modeling in Recommendation Systems

JD Retail Technology

Jun 10, 2025 · Artificial Intelligence

How JD Builds a Scalable AI‑Powered Recommendation Data System with Flink

This article explains JD's complex recommendation system data pipeline—from indexing, sampling, and feature engineering to explainability and real‑time metrics—highlighting challenges such as data consistency, latency, and the use of Flink for massive, low‑latency processing.

Big DataFeature EngineeringFlink

0 likes · 23 min read

How JD Builds a Scalable AI‑Powered Recommendation Data System with Flink

Architect

May 31, 2025 · Artificial Intelligence

Edge Intelligence Implementation in the Vivo Official App: Architecture, Feature Engineering, and Model Deployment

The article details how edge intelligence is applied to the Vivo official app to improve product recommendation on the smart‑hardware floor by abstracting the problem, designing feature engineering pipelines, training TensorFlow models, converting them to TFLite, and deploying inference on mobile devices, while also covering monitoring and performance considerations.

Feature EngineeringModel DeploymentTensorFlow Lite

0 likes · 19 min read

Edge Intelligence Implementation in the Vivo Official App: Architecture, Feature Engineering, and Model Deployment

JD Tech Talk

Apr 30, 2025 · Artificial Intelligence

Adaptive Degradation and Recovery for JD Alliance Recommendation System under High‑Frequency Traffic Spikes

The article presents a comprehensive adaptive degradation and automatic recovery framework for JD Alliance's recommendation system, designed to handle high‑frequency, instantaneous traffic surges during large promotions by combining real‑time monitoring, Wilson‑interval‑based timeout correction, scenario‑aware control, traffic slicing, linear‑programming‑driven chain optimization, and low‑cost business‑agnostic APIs, achieving over 90% reduction in traffic loss and zero incidents.

JD.comLinear ProgrammingReal-time Monitoring

0 likes · 11 min read

Adaptive Degradation and Recovery for JD Alliance Recommendation System under High‑Frequency Traffic Spikes

Model Perspective

Apr 23, 2025 · Artificial Intelligence

Can Math Modeling Transform Language Assessment into Personalized Learning?

This article explores how applying mathematical modeling and fuzzy evaluation to Chinese language proficiency tests like PEYEL can create personalized learning pathways, improve feedback loops, and bridge the gap between assessment results and actionable teaching strategies.

Education TechnologyPersonalized Learningfuzzy logic

0 likes · 12 min read

Can Math Modeling Transform Language Assessment into Personalized Learning?

DeWu Technology

Apr 16, 2025 · Databases

DGraph 2024 Architecture Upgrade and Performance Optimizations

In 2024 DGraph upgraded its architecture by splitting single clusters into multiple business‑specific clusters, adopting a sharded active‑active topology, and replacing its 1:N thread‑pool with an M:N grouped execution model that uses atomic scheduling, while parallelizing FlatBuffer encoding, streamlining SDK conversions, adding DAG debugging, timeline analysis, and dynamic sub‑graph templates to boost scalability, stability and developer productivity.

DAGbackend engineeringdistributed architecture

0 likes · 13 min read

DGraph 2024 Architecture Upgrade and Performance Optimizations

Cognitive Technology Team

Mar 31, 2025 · Artificial Intelligence

Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling

The article explains how Douyin's recommendation system uses machine‑learning and deep‑learning models to predict user actions, assign value weights, and dynamically adjust scores, highlighting both its efficiency in large‑scale content distribution and its inherent limitations compared to human understanding.

AIdeep learningmachine learning

0 likes · 7 min read

Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling

Xiaokun's Architecture Exploration Notes

Mar 24, 2025 · Artificial Intelligence

How to Model Architecture for a High‑Performance Recommendation System

This article walks through business, conceptual, logical, and physical modeling steps to design a recommendation system architecture, detailing value propositions, workflow decomposition, component breakdown, and technology choices to meet reliability, low‑latency, and scalability requirements.

AIarchitecture modelingbusiness modeling

0 likes · 10 min read

How to Model Architecture for a High‑Performance Recommendation System

Bilibili Tech

Jan 17, 2025 · Backend Development

NeighborHash: An Enhanced Batch Query Architecture for Real‑time Recommendation Systems

NeighborHash is a distributed batch‑query architecture for real‑time recommendation systems that combines a cache‑line‑optimized hash table—featuring Lodger Relocation, bidirectional cache‑aware probing, and inline‑chaining—with an NVMe‑backed key‑value service, versioned updates, and asynchronous memory‑access chaining to achieve sub‑microsecond, high‑throughput top‑N retrieval.

AMACBatch QueryDistributed Storage

0 likes · 20 min read

NeighborHash: An Enhanced Batch Query Architecture for Real‑time Recommendation Systems

JD Tech Talk

Jan 14, 2025 · Artificial Intelligence

Advantages and Engineering Implementation of Generative Recommendation Systems Using Large Language Models

This article explains how generative recommendation systems powered by large language models simplify the recommendation pipeline, integrate world knowledge, benefit from scaling laws, and require specialized engineering optimizations such as TensorRT‑LLM deployment, inference acceleration, and hybrid model strategies to achieve low latency and high throughput in real‑world e‑commerce scenarios.

AILLMTensorRT-LLM

0 likes · 10 min read

Advantages and Engineering Implementation of Generative Recommendation Systems Using Large Language Models

Bilibili Tech

Dec 27, 2024 · Big Data

Consistency Architecture for Bilibili Recommendation Model Data Flow

The article outlines Bilibili’s revamped recommendation data‑flow architecture that eliminates timing and calculation inconsistencies by snapshotting online features, unifying feature computation in a single C++ library accessed via JNI, and orchestrating label‑join and sample extraction through near‑line Kafka/Flink pipelines, with further performance gains and Iceberg‑based future extensions.

Big DataFeature EngineeringFlink

0 likes · 12 min read

Consistency Architecture for Bilibili Recommendation Model Data Flow

DataFunSummit

Nov 20, 2024 · Artificial Intelligence

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practice

This article reviews the evolution of large‑model recommendation techniques, analyzes the specific challenges of health‑oriented e‑commerce recommendation, and details practical deployments such as LLM‑enhanced cold‑start recall, DeepI2I expansion, and scaling‑law‑driven CTR models within JD Health.

ctre-commercehealth tech

0 likes · 18 min read

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practice

JD Retail Technology

Nov 6, 2024 · Artificial Intelligence

Explainability Practices in JD Retail Recommendation System

This article describes the definition, architecture, and practical applications of explainability in JD's retail recommendation system, covering ranking, model, and traffic explainability, system challenges, data infrastructure, and specific techniques such as SHAP and Integrated Gradients for interpreting model decisions.

AIRankingexplainability

0 likes · 17 min read

Explainability Practices in JD Retail Recommendation System

Zhuanzhuan Tech

Oct 24, 2024 · Artificial Intelligence

Pre‑Ranking in Recommendation Systems: Model and Sample Optimization Practices at Zhuanzhuan Home Page

This article reviews the role of pre‑ranking in multi‑stage recommendation pipelines, compares dual‑tower and fully‑connected DNN models, discusses negative and positive sample selection strategies, and presents Zhuanzhuan's practical improvements in model architecture and traffic‑pool allocation to boost precision and diversity.

dual‑towermodel optimizationpre‑ranking

0 likes · 16 min read

Pre‑Ranking in Recommendation Systems: Model and Sample Optimization Practices at Zhuanzhuan Home Page

Bilibili Tech

Sep 24, 2024 · Backend Development

Technical Implementation of Bilibili's Game Live Streaming Interactive Features: 'Play Together' and 'Help Me Play'

Bilibili’s game live‑stream platform implements interactive features ‘Play Together’ and ‘Help Me Play’ by using Redis ZSET queues, MySQL persistence, real‑time streamer recommendation, ticket‑based purchase flows, state‑machine order handling, and comprehensive monitoring to ensure reliable, scalable viewer‑streamer gameplay collaboration.

BilibiliMySQLRedis

0 likes · 12 min read

Technical Implementation of Bilibili's Game Live Streaming Interactive Features: 'Play Together' and 'Help Me Play'

DataFunSummit

Sep 16, 2024 · Artificial Intelligence

Multimodal Content Understanding and Cold-Start Practices in NetEase Cloud Music Community Recommendation System

This article details how NetEase Cloud Music leverages multimodal content understanding—using audio models like MusicCLIP and Audio MAE and image‑text fusion via FLAVA—to improve recommendation performance for new content and new users, covering system architecture, cold‑start solutions, and future AI‑driven directions.

AI modelsCold Startaudio representation

0 likes · 15 min read

Multimodal Content Understanding and Cold-Start Practices in NetEase Cloud Music Community Recommendation System

Sohu Tech Products

Aug 28, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

EasyRec, Alibaba Cloud’s modular recommendation framework, unifies configurable data, embedding, dense, and output layers on MaxCompute, EMR, and DLC, and speeds training with deduplication, EmbeddingParallel sharding, lock‑free hash tables, GPU embeddings, and AMX BF16, while inference benefits from operator fusion, low‑precision AVX/AMX kernels, compact caches, batch merging, and network compression, enabling real‑time online learning and delivering higher recommendation quality at lower cost in e‑commerce.

Alibaba CloudEasyRecTraining Optimization

0 likes · 14 min read

EasyRec Recommendation Algorithm Training and Inference Optimization

JD Retail Technology

Aug 14, 2024 · Artificial Intelligence

Adaptive Degradation and Recovery for JD Alliance Recommendation System During High‑Volume Promotions

This article describes how JD Alliance built an adaptive degradation and automatic recovery framework for its recommendation system to handle sudden, large‑scale traffic spikes during major sales events, ensuring stability while minimizing recommendation loss through real‑time monitoring, scenario‑aware control, and linear‑programming‑based pipeline orchestration.

JD.comLinear ProgrammingReal-time Monitoring

0 likes · 9 min read

Adaptive Degradation and Recovery for JD Alliance Recommendation System During High‑Volume Promotions

DataFunTalk

Aug 7, 2024 · Artificial Intelligence

Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results

This article presents NetEase Cloud Music's multi‑scenario recommendation modeling work, detailing background, overall system architecture, key modules, modeling goals, technical difficulties, performance improvements, future outlook, and a comprehensive Q&A session that addresses practical deployment challenges.

AB testingAIModel Architecture

0 likes · 14 min read

Multi-Scenario Modeling for NetEase Cloud Music Recommendation: Architecture, Challenges, and Results

AntTech

Jun 30, 2024 · Artificial Intelligence

AI Volunteer Assistant for College Entrance Exam Using the agentUniverse Multi‑Agent Framework

The article introduces an AI‑powered “Volunteer Assistant” built on the agentUniverse multi‑agent framework, detailing how it outperforms existing tools by integrating a specialized SOP, multi‑agent collaboration, and employment‑market analysis to provide precise, personalized college‑major recommendations for high‑school graduates.

AIAgentUniverseCollege Admissions

0 likes · 7 min read

AI Volunteer Assistant for College Entrance Exam Using the agentUniverse Multi‑Agent Framework