online learning

DataFunSummit

Nov 22, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec’s recommendation system architecture, detailing training and inference optimizations, embedding parallelism, CPU/GPU placement strategies, online learning pipelines, and network compression techniques that together improve scalability, latency, and cost efficiency.

Distributed SystemsEasyRecTraining Optimization

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EasyRec Recommendation Algorithm Training and Inference Optimization

DataFunTalk

Oct 11, 2024 · Artificial Intelligence

E‑commerce Innovation and Data Governance: Summaries of Recent Research Topics

This article compiles concise overviews of recent e‑commerce research, covering real‑time online learning re‑ranking models, causal inference for user growth, full‑link data lineage, TikTok's data governance and attribution solutions, Volcano Engine's metric management, AI Agent applications on 1688, and XinXuan Group's live‑stream data architecture.

AIcausal inferencedata governance

0 likes · 5 min read

E‑commerce Innovation and Data Governance: Summaries of Recent Research Topics

iQIYI Technical Product Team

Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

CTR predictionadvertisingdeep learning

0 likes · 9 min read

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

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

DataFunTalk

Aug 26, 2024 · Artificial Intelligence

EasyRec Recommendation Algorithm Training and Inference Optimization

This article presents a comprehensive overview of EasyRec's recommendation system architecture, detailing training and inference optimizations, distributed deployment strategies, operator fusion techniques, online learning pipelines, and network-level improvements to enhance performance and scalability.

AIDistributed SystemsTraining Optimization

0 likes · 15 min read

JD Tech

Jun 23, 2024 · Artificial Intelligence

Applying Large Models to Recommendation Systems: Strategies, Challenges, and E‑commerce Case Study

This article examines how large pre‑trained models such as GPT‑4 and BERT are integrated into modern recommendation systems, detailing their advantages, implementation strategies, real‑world e‑commerce case studies, and the technical and privacy challenges that must be addressed for effective deployment.

Artificial IntelligenceLarge ModelsRecommendation systems

0 likes · 14 min read

Applying Large Models to Recommendation Systems: Strategies, Challenges, and E‑commerce Case Study

DataFunSummit

Nov 27, 2023 · Artificial Intelligence

Online Learning with Alink Model Flow: From Fundamentals to Model Flow 1.0 and 2.0

This article introduces Alibaba's Alink platform and its online learning capabilities, discusses common challenges in machine‑learning pipelines, explains Alink’s algorithm‑to‑application connection, various computation modes, usage methods, and details the evolution from Model Flow 1.0 to the more versatile Model Flow 2.0, including pipeline integration, incremental training, and embedding prediction services.

AlinkFlinkPipeline

0 likes · 9 min read

Online Learning with Alink Model Flow: From Fundamentals to Model Flow 1.0 and 2.0

DataFunSummit

Nov 21, 2023 · Artificial Intelligence

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

This article presents an in‑depth overview of Tencent's TRS automatic hyperparameter tuning, covering background, challenges, the evolution from Bayesian optimization to evolution strategies and reinforcement learning, a systematic platform solution, real‑world deployment results, and a Q&A session.

Bayesian Optimizationevolution strategieshyperparameter tuning

0 likes · 20 min read

Automatic Hyperparameter Tuning in Tencent Recommendation System (TRS): Techniques, Evolution, and Practice

Alimama Tech

Sep 20, 2023 · Artificial Intelligence

Exploring Model Dynamics for Accumulative Poisoning Detection

The paper, a joint effort by Alibaba Mama and HKBU TMLR, shows that monitoring model dynamics—specifically a newly defined memorization‑discrepancy metric—can reveal hidden accumulative poisoning attacks in online advertising streams, and introduces a discrepancy‑aware correction algorithm that consistently outperforms existing defenses across benchmark datasets.

defense algorithmsmachine learning securitymodel dynamics

0 likes · 13 min read

Exploring Model Dynamics for Accumulative Poisoning Detection

DataFunTalk

Nov 15, 2022 · Artificial Intelligence

Flink ML: Iterative Execution Engine, Design, API, and Efficient Algorithm Library

This article introduces Flink ML, a DataStream‑based iterative engine and machine‑learning algorithm library, covering its overview, iterative execution engine design and API, performance comparisons with Spark ML, online logistic regression and K‑Means demos, and future development roadmap.

Big DataFlinkIterative Engine

0 likes · 22 min read

Flink ML: Iterative Execution Engine, Design, API, and Efficient Algorithm Library

Alimama Tech

Aug 24, 2022 · Artificial Intelligence

Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

The authors introduce AGE, an adversarial‑gradient‑driven exploration framework that injects uncertainty‑scaled perturbations into ad embeddings to approximate the downstream learning effect, combines Monte‑Carlo dropout uncertainty, a dynamic gating unit, and achieves up to 15 % offline gains and 6 % online CTR improvement over strong baselines.

CTR predictionRecommendation systemsadversarial gradient

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Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction

Alimama Tech

Apr 27, 2022 · Artificial Intelligence

DEFUSE and Bi-DEFUSE: Unbiased Delayed‑Feedback Modeling for CVR Prediction

The paper introduces DEFUSE and its multi‑task extension Bi‑DEFUSE, unbiased delayed‑feedback CVR models that correct label bias via rigorous importance‑sampling and a latent fake‑negative variable, achieving superior offline performance and a 2 % CVR lift in online deployment compared with existing industry baselines.

Bi-DEFUSECVRDEFUSE

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DEFUSE and Bi-DEFUSE: Unbiased Delayed‑Feedback Modeling for CVR Prediction

Tencent Cloud Developer

Apr 7, 2022 · Artificial Intelligence

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

The article surveys the re‑ranking stage of modern recommendation pipelines, detailing its architecture after recall and precise ranking, and examining how shuffling and diversity improve user experience, while multi‑task fusion, context‑aware learning‑to‑rank, real‑time online learning, and traffic‑control strategies balance accuracy, efficiency, and business responsiveness.

algorithmdiversitymachine learning

0 likes · 15 min read

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

DaTaobao Tech

Mar 22, 2022 · Artificial Intelligence

Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel

The article details Alibaba’s end‑to‑end online‑learning pipeline for real‑time ranking in the Taobao home‑decor channel, covering UT log parsing, full‑feature extraction, ODL sample creation, xDeepCTR model training, and deployment, which yielded up to 7.8% CTR improvement and demonstrates the value of rapid model adaptation.

Alibabamodel trainingonline learning

0 likes · 15 min read

Online Learning for Real‑Time Ranking in Alibaba's Home‑Decor Channel

IEG Growth Platform Technology Team

Dec 20, 2021 · Artificial Intelligence

Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems

This article presents a three‑part technical guide covering the fundamentals of computational advertising and real‑time bidding, detailed offline pCTR model training pipelines with feature engineering, calibration and model structure improvements, and advanced online learning techniques such as parameter freezing, sample replay and knowledge distillation, all aimed at boosting CTR performance and reducing bias in large‑scale ad platforms.

CTR predictionFeature Engineeringadvertising

0 likes · 37 min read

Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems

DataFunTalk

Dec 17, 2021 · Artificial Intelligence

Applying Reinforcement Learning to Solve Cold‑Start Problems in 58.com Job Recruitment

This talk explains how 58.com’s massive blue‑collar recruitment platform uses reinforcement‑learning techniques—including multi‑armed bandits, contextual MAB, and linear UCB—to address cold‑start and interest‑divergence challenges, describes the system architecture, offline evaluation, online deployment, and reports an 8% uplift in new‑user conversion.

Cold StartMulti-armed banditcontextual MAB

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Applying Reinforcement Learning to Solve Cold‑Start Problems in 58.com Job Recruitment

DataFunTalk

Mar 1, 2021 · Artificial Intelligence

Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink

The article describes JD's end‑to‑end online learning pipeline for retail search, covering the background, system architecture, real‑time feature collection, sample stitching, Flink‑based incremental training, parameter updates, and full‑link monitoring to achieve low‑latency, high‑accuracy model serving.

Feature EngineeringFlinkmodel serving

0 likes · 9 min read

Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink

DataFunTalk

Feb 21, 2021 · Artificial Intelligence

Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution

This article reviews the development history, technical routes, and recent breakthroughs of pre‑ranking (coarse ranking) in large‑scale advertising systems, focusing on Alibaba's COLD (Computing‑power‑cost‑aware Online and Lightweight Deep) framework, its model design, engineering optimizations, experimental results, and future research directions.

COLDadvertisingmachine learning

0 likes · 20 min read

Advances in Pre‑Ranking for Large‑Scale Advertising: The COLD Framework and Its Technical Evolution

DataFunTalk

Nov 19, 2020 · Artificial Intelligence

58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution

This talk details the design, data pipeline, feature engineering, model evolution, and operational insights of the 58 Tongzhen home feed recommendation system, covering its architecture, localization strategies, recall and ranking models, online learning, and future directions for AI-driven content delivery in the down‑market.

AIFeature Engineeringdown‑market

0 likes · 34 min read

58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution

DataFunSummit

Nov 8, 2020 · Artificial Intelligence

Architecture and Evolution of 58 Tongzhen Local Feed Recommendation System

This article details the design, data pipeline, feature engineering, model development, and iterative optimization of the 58 Tongzhen local feed recommendation system, covering business background, user profiling, recall strategies, ranking models such as XGBoost, XDeepFM, and online learning, and future directions.

AIBig DataFeature Engineering

0 likes · 33 min read