Tagged articles

455 articles

Page 4 of 5

Dec 17, 2020 · Artificial Intelligence

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

The article presents a comprehensive study of re‑ranking in large‑scale industrial recommendation pipelines, identifies four key challenges—context awareness, permutation specificity, computational complexity, and business constraints—and proposes a two‑stage List Retrieval System that combines fast sequence search and a generative re‑ranking network with a deep context‑wise model, achieving significant online gains across multiple Taobao feed scenarios.

Context-AwareDeep LearningIndustrial AI

0 likes · 28 min read

Context‑Aware Re‑ranking in Industrial Recommendation Systems: Design and Practice of a List Retrieval System

Ctrip Technology

Dec 10, 2020 · Artificial Intelligence

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

This article presents a comprehensive study on automatically extracting theme‑based recommendation reasons for travel content, detailing a three‑stage retrieval framework, the advantages of interactive matching models over classification, rule‑based and back‑translation data augmentation techniques, and various model optimization strategies including priors, transfer learning, seed selection, optimizer choice, and layer‑wise learning rates.

AIRecommendation Systemsdata augmentation

0 likes · 19 min read

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

Meituan Technology Team

Dec 3, 2020 · Artificial Intelligence

Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020

Meituan’s search and NLP team announced that six knowledge‑graph papers—covering query‑aware tip generation, BERT‑based ranking, multi‑modal and sequential recommendation, conversational recommendation, and graph‑embedding for personalized product search—were accepted at CIKM 2020, resulting from university collaborations and already deployed to boost Meituan’s search, recommendation and product‑search services.

BERTCIKM 2020Knowledge Graph

0 likes · 13 min read

Meituan Knowledge Graph Group's Six Papers Accepted at CIKM 2020

DataFunTalk

Dec 2, 2020 · Artificial Intelligence

How Recommendation Algorithms Drive User Growth in Content Feed Systems

This article examines how low‑level recommendation algorithm techniques can upgrade content‑feed systems to boost user growth, covering problem analysis, growth factors, personalization upgrades, cold‑start mechanisms, bias mitigation via causal inference, and utility‑driven user profiling.

Recommendation Systemsalgorithm designcausal inference

0 likes · 14 min read

How Recommendation Algorithms Drive User Growth in Content Feed Systems

DataFunTalk

Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

EmbeddingRecommendation Systemsgraph neural networks

0 likes · 32 min read

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

DataFunSummit

Nov 24, 2020 · Artificial Intelligence

Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms

This article examines the unique properties of novel literature, the difficulties of tag‑based representation, and how multi‑modal AI techniques—including dual‑tower models, feature fusion, clustering, and YouTube‑style DNN recall—are applied to improve recommendation accuracy and user decision‑making.

Multi-modal AIRecommendation SystemsYouTube DNN

0 likes · 7 min read

Understanding Novel Literature Recommendation: Characteristics, Tagging Challenges, and Multi‑Modal AI Algorithms

DataFunTalk

Nov 12, 2020 · Artificial Intelligence

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

This article explains how reinforcement learning, with its focus on maximizing long‑term reward, can improve recommendation system mixing by covering basic RL concepts, differences from supervised learning, multi‑armed bandit approaches, practical OpenAI Gym experiments, new AUC metrics, online gains, and advanced model optimizations.

OpenAI GymQ-LearningRecommendation Systems

0 likes · 10 min read

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

Bitu Technology

Nov 10, 2020 · Artificial Intelligence

Key Takeaways from RecSys 2020: Conference Organization and Notable Research Highlights

The article reviews RecSys 2020’s shift to a virtual format, highlights the organizers’ use of tools like Whova and Gather.town, and summarizes several industrial and academic research breakthroughs presented at the conference, including PURS, behavior‑based popularity ranking, contextual item‑to‑item recommendation, counterfactual learning, debiasing techniques, and a large‑scale bandit dataset.

Academic PapersIndustrial ResearchRecSys 2020

0 likes · 9 min read

Key Takeaways from RecSys 2020: Conference Organization and Notable Research Highlights

Yuewen Technology

Nov 10, 2020 · Artificial Intelligence

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.

LambdaMARTLearning-to-RankLightGBM

0 likes · 9 min read

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

JD Cloud Developers

Oct 29, 2020 · Artificial Intelligence

How JD Leverages Knowledge Graphs for Better E‑commerce Interest Recall

JD’s recommendation team outlines three key innovations—knowledge‑graph‑based interest recall, enhanced CTR estimation with a DRM module, and a listwise ranking strategy—that together address user‑interest expansion challenges in e‑commerce, especially for cold‑start items, long‑tail products, and dynamic promotional scenarios.

CTR estimationKnowledge GraphRecommendation Systems

0 likes · 21 min read

How JD Leverages Knowledge Graphs for Better E‑commerce Interest Recall

Hulu Beijing

Oct 26, 2020 · Artificial Intelligence

Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings

The Hulu AI Class event showcased a series of technical talks covering large‑scale graph neural network optimizations, multi‑factor video ad placement algorithms, recommendation and search engine techniques, machine‑learning‑driven video codec improvements, and advanced content‑embedding methods, highlighting practical engineering experiences from Hulu’s Beijing office.

Ad TargetingRecommendation Systemscontent embedding

0 likes · 9 min read

Hulu’s AI Innovations: Graph Neural Networks, Ad Targeting & Content Embeddings

360 Zhihui Cloud Developer

Sep 16, 2020 · Artificial Intelligence

How TensorNet Supercharges Sparse Feature Training on TensorFlow

TensorNet is a TensorFlow‑based distributed training framework optimized for massive sparse‑feature models in advertising and recommendation, dramatically reducing parameter sync overhead, enabling near‑infinite feature dimensions, cutting training time from hours to minutes, and boosting inference performance by up to 35%.

AIRecommendation SystemsTensorFlow

0 likes · 10 min read

How TensorNet Supercharges Sparse Feature Training on TensorFlow

58UXD

Sep 15, 2020 · Artificial Intelligence

How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights

This article explores the fundamentals and evaluation of recommendation systems, detailing their definition, key performance dimensions such as accuracy, diversity, novelty, serendipity, trust, and real‑time utility, and presents a practical case study from 58.com with reflections on methodology and future improvements.

Evaluation MetricsRecommendation SystemsUser experience

0 likes · 12 min read

How to Evaluate Recommendation Systems: Metrics, Case Study, and Insights

DataFunTalk

Aug 30, 2020 · Big Data

Large-Scale Recommendation System Feature Engineering and Optimization with Spark and FESQL

This article explains how large-scale recommendation systems rely on efficient feature engineering, describes the three-layer architecture (offline, stream, online), and details how Spark SQL and the LLVM‑optimized FESQL engine improve performance and ensure offline‑online feature consistency.

Big DataFESQLLLVM

0 likes · 13 min read

Large-Scale Recommendation System Feature Engineering and Optimization with Spark and FESQL

DataFunTalk

Aug 27, 2020 · Artificial Intelligence

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

This article explains the fundamental differences between computational advertising and recommendation systems, outlines the distinct problems each field addresses, and surveys the most widely used advertising models—including traditional machine‑learning approaches, deep‑learning architectures, and hybrid solutions—providing practical insights for engineers in both domains.

AICTR modelsDeep Learning

0 likes · 11 min read

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

DataFunTalk

Aug 27, 2020 · Artificial Intelligence

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

This article explains Alibaba’s User Interest Center approach to real‑time model serving, detailing how it separates offline sequence modeling from lightweight online inference, uses an online interest‑embedding store, and dramatically reduces latency for recommendation models such as DIEN and MIMN.

AlibabaEmbeddingModel Serving

0 likes · 8 min read

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

Meituan Technology Team

Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingGraph ModelingKDD Cup

0 likes · 23 min read

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

DataFunTalk

Jul 31, 2020 · Artificial Intelligence

WeChat 'Kan Kan' Content Understanding: Architecture and Techniques for Recommendation

This article details the technical architecture behind WeChat's 'Kan Kan' content understanding platform, covering text and multimedia analysis, tag extraction, entity recognition, knowledge graph construction, and how these components enhance recommendation recall, ranking, and user engagement across the ecosystem.

Knowledge GraphMultimodal AIRecommendation Systems

0 likes · 46 min read

WeChat 'Kan Kan' Content Understanding: Architecture and Techniques for Recommendation

ITPUB

Jul 25, 2020 · Backend Development

How SimSvr Achieves Billion‑Scale Real‑Time ANN Search for Recommendations

SimSvr is a high‑performance, distributed feature‑retrieval component designed for recommendation systems that supports billion‑scale indexes, sub‑millisecond query latency, real‑time and batch updates, multi‑model AB‑testing, and advanced filtering, all while running on Tencent's production workloads.

ANNRecommendation Systemsfeature retrieval

0 likes · 17 min read

How SimSvr Achieves Billion‑Scale Real‑Time ANN Search for Recommendations

Meituan Technology Team

Jul 23, 2020 · Artificial Intelligence

43rd ACM SIGIR 2020 Conference Overview

The 43rd ACM SIGIR 2020 International Conference on Research and Development in Information Retrieval, a premier CCF A‑class event, will be held virtually from July 25‑30, featuring theoretical and applied research on knowledge graph construction, explainable recommendation, and content generation, with a keynote by Meituan Waimai’s senior algorithm expert Maodi Hu.

ACM SIGIRAI applicationsKnowledge Graph

0 likes · 4 min read

DataFunTalk

Jul 19, 2020 · Product Management

Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms

This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.

Recommendation Systemsdata analysismatching algorithms

0 likes · 30 min read

Stranger Social Apps: Business Insights, Data‑Driven Modeling, and Matching Algorithms

Didi Tech

Jul 16, 2020 · Operations

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

The paper describes Didi’s online driver‑repositioning system that treats idle‑driver dispatch as a recommender problem, generating candidate destinations, scoring tasks with a marginal‑gain model, and selecting optimal assignments via a minimum‑cost‑flow optimizer, which in live A/B tests boosted driver efficiency, earnings, and satisfaction while reducing empty cruising.

AB testingRecommendation Systemsdriver repositioning

0 likes · 11 min read

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

DataFunTalk

Jul 12, 2020 · Artificial Intelligence

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

This article surveys the role of social tagging (folksonomy) in modern recommendation systems, describing how user‑generated tags form a three‑dimensional "tag cube" that can be combined with rating matrices, and reviewing a range of algorithms—including neighbor‑based, ranking (FolkRank/SocialRank), content‑based, linear regression, and matrix‑factorization approaches—while also discussing tag selection, noise handling, and scalability challenges.

AIRecommendation Systemscollaborative filtering

0 likes · 35 min read

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

Sohu Tech Products

Jul 8, 2020 · Artificial Intelligence

Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms

This article explains the fundamentals of recommendation systems, discusses early hotness‑based approaches, describes modern architectures with recall and ranking stages, reviews collaborative‑filtering techniques, matrix factorization, deep learning models such as NCF and NeuMF, and details how these methods are applied and optimized for live‑streaming services.

AIDeep LearningRecommendation Systems

0 likes · 30 min read

Overview of Recommendation Systems and Their Evolution in Live Streaming Platforms

DataFunTalk

Jun 27, 2020 · Artificial Intelligence

What AI Brings to Financial Investment: Limitations of Recommendation Models Compared to Live‑Streaming Commerce

The article examines the rapid growth of live‑streaming e‑commerce, explains the trust‑based dynamics of influencers, outlines standard recommendation‑system metrics such as accuracy, recall, diversity and explainability, and argues that these models fall short of long‑term user utility because they are driven by short‑term commercial goals, highlighting economic and neuroscientific perspectives on preference randomness.

MetricsRecommendation SystemsTrust

0 likes · 13 min read

What AI Brings to Financial Investment: Limitations of Recommendation Models Compared to Live‑Streaming Commerce

DataFunTalk

Jun 21, 2020 · Artificial Intelligence

Comprehensive Guide to Recommendation Engine Types and Techniques

This article provides a detailed overview of various recommendation system types—including neighbor-based, personalized, content-based, contextual, hybrid, and model-based approaches—explaining their principles, advantages, disadvantages, and practical examples with formulas and visual illustrations for real-world applications.

Context-AwareHybridRecommendation Systems

0 likes · 28 min read

Comprehensive Guide to Recommendation Engine Types and Techniques

iQIYI Technical Product Team

Jun 19, 2020 · Artificial Intelligence

From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey

iQIYI’s recommendation team transformed its offline, slow‑query system into a real‑time engine by sharding databases, adding caching, and adopting Kafka, Spark‑Streaming and Flink, cutting peak timeout from 4% to under 0.3%, delivering second‑level personalized, diverse, high‑quality video suggestions while keeping engineers close to the front‑line.

Recommendation SystemsiQIYImachine learning

0 likes · 7 min read

From Offline to Real-Time Recommendation: iQIYI’s Scalable Machine Learning Journey

NetEase Media Technology Team

Jun 12, 2020 · Artificial Intelligence

Semantic Text Understanding for NetEase News Feed Recommendation

NetEase improves its news‑feed recommendation by applying a multi‑stage semantic text understanding pipeline—lexical analysis, hierarchical content tagging, and quality filtering—using two‑level classifiers, LDA‑based topic modeling, multi‑label concept and entity extraction, and dense vector representations to better capture user interests and boost personalization performance.

NLPRecommendation Systemsfeature engineering

0 likes · 9 min read

Semantic Text Understanding for NetEase News Feed Recommendation

DataFunTalk

Jun 4, 2020 · Artificial Intelligence

Exploring Federated Recommendation Algorithms and Their Applications

This article introduces the challenges of traditional centralized recommendation systems, explains the principles and implementations of federated recommendation algorithms—including vertical and horizontal federated matrix factorization and factorization machines—using WeBank’s open-source FATE platform, and discusses cloud services, practical use cases, and performance benefits.

AIFATEFederated Learning

0 likes · 13 min read

Exploring Federated Recommendation Algorithms and Their Applications

Sohu Tech Products

May 27, 2020 · Artificial Intelligence

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

This article provides a comprehensive overview of graph embedding methods—including DeepWalk, LINE, node2vec, and EGES—explaining their algorithms, random‑walk strategies, proximity definitions, incorporation of side information, and their applications in large‑scale recommendation systems.

DeepWalkRecommendation Systemsgraph embedding

0 likes · 20 min read

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

DataFunTalk

May 26, 2020 · Artificial Intelligence

Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights

This article reviews how knowledge distillation—using a large teacher model to guide a smaller student model—can be applied across the recall, coarse‑ranking, and fine‑ranking stages of recommendation systems, detailing logits‑based and feature‑based approaches, joint and two‑stage training, and point‑wise, pair‑wise, and list‑wise loss designs.

Recommendation Systemsknowledge distillationmachine learning

0 likes · 31 min read

Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights

DataFunTalk

May 23, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Based on academic and industry experience, iQIYI has designed a deep semantic representation learning framework that integrates multimodal side information and deep models such as Transformers and graph neural networks, improving recall, ranking, deduplication, diversity and semantic matching across recommendation and search scenarios.

Deep LearningRecommendation SystemsSearch

0 likes · 27 min read

iQIYI Deep Semantic Representation Learning Framework for Video Recommendation and Search

Alibaba Cloud Developer

May 21, 2020 · Artificial Intelligence

How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals

This article examines the DeepMatch retrieval model for Tmall Genie music recommendation, detailing how incorporating user feedback such as play‑rate and query intent signals via multi‑task learning and feedback‑aware self‑attention improves recall accuracy and reduces negative recommendations, while also discussing embedding factorization, loss functions, and distributed training optimizations.

Deep LearningRecommendation SystemsSelf-Attention

0 likes · 18 min read

How DeepMatch Boosts Music Recommendations with Play Rate and Intent Signals

DataFunTalk

May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictionDeep LearningRecommendation Systems

0 likes · 10 min read

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

JD Retail Technology

May 13, 2020 · Artificial Intelligence

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

JD announced that two of its research papers—one on a hierarchical reinforcement‑learning framework for multi‑objective recommendation (MaHRL) and another on an attention‑based model for delayed‑feedback conversion‑rate prediction (pCVR)—were accepted as full papers at the prestigious IJCAI2020 and SIGIR2020 conferences, highlighting the company's strong AI capabilities.

Recommendation Systemsartificial intelligenceconversion rate prediction

0 likes · 6 min read

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

DataFunTalk

May 8, 2020 · Artificial Intelligence

Distributed Machine Learning Framework GDBT for High‑Dimensional Real‑Time Recommendation Systems

The article explains how the fourth paradigm's distributed machine learning framework GDBT tackles the massive data, high‑dimensional features, and real‑time requirements of modern recommendation systems by leveraging heterogeneous computing, parameter servers, RDMA networking, and optimized workloads.

GDBTParameter ServerRDMA

0 likes · 18 min read

Distributed Machine Learning Framework GDBT for High‑Dimensional Real‑Time Recommendation Systems

DataFunTalk

Apr 27, 2020 · Artificial Intelligence

Graph-Based Recommendation Algorithms and Cold‑Start Solutions

This article presents a comprehensive overview of graph‑based recommendation techniques, including collaborative filtering, graph embedding, side‑information enhanced embeddings, two‑tower DSSM models, and practical cold‑start strategies from Alibaba and Airbnb, followed by a mixed model and Q&A session.

AIRecommendation Systemscold start

0 likes · 14 min read

Graph-Based Recommendation Algorithms and Cold‑Start Solutions

DataFunTalk

Apr 23, 2020 · Artificial Intelligence

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

The article explains how Alibaba Entertainment leverages causal inference and uplift modeling to build unbiased user‑cf recommendation algorithms that model user states and upgrade personalized distribution, achieving significant click‑through and re‑activation gains for long‑video services like Youku.

Recommendation SystemsVideo platformcausal inference

0 likes · 13 min read

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

DataFunTalk

Apr 21, 2020 · Artificial Intelligence

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

This article surveys the application of attention mechanisms in deep learning recommendation systems, reviewing models such as AFM, DIN, DIEN, DSIN, Behavior Sequence Transformer, Deep Spatio‑Temporal Networks, and ATRank, and discusses their architectures, attention types, advantages, and limitations.

CTR predictionDeep LearningRecommendation Systems

0 likes · 10 min read

Attention Mechanisms in Deep Learning Recommendation Models: A Survey

DataFunTalk

Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Deep LearningGraph Neural NetworkKnowledge Graph

0 likes · 14 min read

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

DataFunTalk

Apr 6, 2020 · Artificial Intelligence

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

DeepMatch is an open‑source Python library that implements several mainstream deep‑learning based recall‑matching algorithms, provides easy installation via pip, detailed usage examples with code, and supports exporting user and item vectors for ANN search, making it ideal for rapid experimentation and learning in recommendation systems.

ANNDeep LearningPython

0 likes · 10 min read

Introducing DeepMatch: An Open‑Source Library for Deep Retrieval Matching Algorithms

DataFunTalk

Mar 13, 2020 · Artificial Intelligence

Knowledge Graph Assisted Personalized Recommendation Systems

Personalized recommendation systems, essential for modern internet platforms, can be enhanced by knowledge graphs which provide auxiliary information to improve accuracy, diversity, and explainability, with various methods such as embedding-based (DKN, MKR), path-based, and hybrid approaches like RippleNet and KGCN.

KG-awareKnowledge GraphRecommendation Systems

0 likes · 21 min read

Knowledge Graph Assisted Personalized Recommendation Systems

ITPUB

Mar 11, 2020 · Artificial Intelligence

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive technical overview of Toutiao’s recommendation system, covering its three‑dimensional modeling approach, feature engineering, user‑tag pipelines, real‑time training infrastructure, evaluation methodology, and content‑safety mechanisms.

A/B testingContent SafetyReal-time Training

0 likes · 17 min read

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

Liangxu Linux

Mar 9, 2020 · Artificial Intelligence

Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds

ByteDance’s recommendation system models user satisfaction as a function of content, user, and context features, employing diverse algorithms—from logistic regression to deep learning—while leveraging real‑time training, hierarchical text classification, dynamic user tagging, rigorous A/B testing, and multi‑layer content safety checks to deliver personalized feeds at massive scale.

Content SafetyReal-time TrainingRecommendation Systems

0 likes · 19 min read

Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds

JD Tech Talk

Mar 9, 2020 · Artificial Intelligence

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.

Deep LearningKnowledge GraphMultimodal AI

0 likes · 6 min read

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

Qunar Tech Salon

Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR predictionDeep LearningRecommendation Systems

0 likes · 12 min read

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

Tianxing Digital Tech User Experience

Feb 28, 2020 · Fundamentals

How User Guidance Boosts Product Experience: Design Principles and Strategies

This article explains how various user‑guidance techniques—layout modularity, middle‑option bias, task‑based prompts, rebate incentives, herd‑mentalities, and behavior‑driven recommendations—can steer decisions, accelerate conversions, and enhance overall product experience.

Goldilocks principleProduct DesignRecommendation Systems

0 likes · 6 min read

How User Guidance Boosts Product Experience: Design Principles and Strategies

DataFunTalk

Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Deep LearningOnline LearningRanking Models

0 likes · 13 min read

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

DataFunTalk

Jan 21, 2020 · Artificial Intelligence

How to Enhance Real-Time Updating of Recommendation System Models

The article examines various techniques—including full, incremental, online, and local updates—as well as client‑side embedding refreshes to improve the real‑time performance of recommendation system models, balancing freshness with global optimality.

AIIncremental LearningOnline Learning

0 likes · 9 min read

How to Enhance Real-Time Updating of Recommendation System Models

Huajiao Technology

Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

This article reviews the evolution of ranking models in modern recommendation systems, covering traditional linear models, factorization machines, tree‑based GBDT+LR, and a range of deep learning architectures such as Wide&Deep, DeepFM, DCN, xDeepFM, DIN, as well as multi‑task frameworks like ESMM and MMOE, and finally illustrates their practical deployment in a live streaming platform.

Deep LearningRecommendation Systemsfeature engineering

0 likes · 20 min read

Overview of Ranking Algorithms in Recommendation Systems

DataFunTalk

Jan 6, 2020 · Artificial Intelligence

Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling

The article explains Weibo’s O‑Series advertising system, detailing its three‑part strategy of smart bidding, intelligent targeting, and ROI modeling, the underlying machine‑learning techniques such as deep‑FM, dual‑tower and PID control, and how these components optimize show, click, conversion rates and advertiser ROI.

AdvertisingROIRecommendation Systems

0 likes · 14 min read

Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling

DataFunTalk

Dec 30, 2019 · Artificial Intelligence

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

This article surveys recent advances in recommendation system technology, covering the evolution from a two‑stage recall‑ranking pipeline to a four‑stage architecture, and detailing emerging trends in model‑based recall, user‑behavior sequence modeling, knowledge‑graph integration, graph neural networks, advanced ranking models, multi‑objective optimization, multimodal fusion, and listwise re‑ranking.

Knowledge GraphRecommendation Systemsgraph neural networks

0 likes · 45 min read

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

DataFunTalk

Dec 30, 2019 · Artificial Intelligence

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications

Recent industrial advances in multi-task learning for recommendation systems, including Alibaba's ESMM and DUPN, Meituan's deep ranking, Google's MMoE, YouTube's multi-objective ranking, and Zhihu's multi-goal model, demonstrate how shared embeddings and specialized loss functions improve CTR, CVR, and user engagement metrics.

AICTRCVR

0 likes · 14 min read

Multi-Task Learning Models for Recommendation Systems: A Survey of Industrial Applications

DataFunTalk

Dec 24, 2019 · Artificial Intelligence

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

This article surveys the development of recall modules in large‑scale recommendation systems, covering traditional item‑based collaborative filtering, single‑embedding DNN and dual‑tower approaches, multi‑interest capsule networks, graph‑based embeddings, long‑short term interest modeling, and the tree‑structured TDM framework for efficient deep matching.

Deep LearningRecommendation Systemsgraph embedding

0 likes · 14 min read

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

DataFunTalk

Dec 20, 2019 · Artificial Intelligence

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

The article presents AutoCross, a system that automatically generates and selects high‑order feature crossings for tabular data using multi‑granularity discretization, beam search, field‑wise logistic regression and successive mini‑batch gradient descent, achieving superior accuracy and efficiency in large‑scale recommendation scenarios.

AutoCrossBeam SearchRecommendation Systems

0 likes · 10 min read

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

DataFunTalk

Dec 2, 2019 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques

This article explains how Tencent tackles content understanding for personalized feed recommendation by combining traditional classification, keyword, and entity methods with deep learning embeddings, introducing an interest graph composed of taxonomy, concept, entity, and event layers to capture full context and infer user consumption intent.

NLPRecommendation Systemscontent understanding

0 likes · 14 min read

Content Understanding for Personalized Feed Recommendation: Interest Graph and Techniques

DataFunTalk

Nov 26, 2019 · Artificial Intelligence

Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

This article surveys two neural news recommendation approaches—NAML, which uses multi‑view learning to fuse heterogeneous news information, and NPA, which incorporates personalized attention for both words and news items—demonstrating their superior performance over strong baselines on real‑world MSN news data through extensive experiments and visual analyses.

AIDeep LearningRecommendation Systems

0 likes · 11 min read

Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

DataFunTalk

Oct 16, 2019 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

This article details Meitu's large‑scale personalized recommendation pipeline, describing the business scenario, challenges of massive data, latency and long‑tail distribution, and the application of deep learning techniques such as Item2vec, YouTubeNet, dual‑tower DNN, NFM, NFwFM and multi‑task learning to improve click‑through rate, conversion and user engagement.

Deep LearningRecommendation Systemslarge scale

0 likes · 20 min read

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

Ctrip Technology

Oct 11, 2019 · Artificial Intelligence

Intelligent Content Extraction and Generation Practices on Ctrip's Marco Polo AI Platform

This article details Ctrip's AI‑driven Marco Polo platform, describing how large‑scale NLP pipelines combine extraction, richness evaluation, semantic matching and deep‑learning generation (CopyNet, TA‑seq2seq) to produce high‑quality recommendation reasons across multiple product scenarios.

Content ExtractionNLPRecommendation Systems

0 likes · 16 min read

Intelligent Content Extraction and Generation Practices on Ctrip's Marco Polo AI Platform

Alibaba Cloud Developer

Oct 10, 2019 · Artificial Intelligence

How Joint Optimization of Tree-Based Indexes Boosts Large-Scale Recommendation Accuracy

This article introduces JTM, a joint optimization framework that simultaneously learns deep scoring models and tree-structured indexes, addressing the limitations of traditional recommendation pipelines and demonstrating significant precision and recall gains on large-scale datasets such as Amazon Books and UserBehavior.

Deep LearningRecommendation Systemsjoint optimization

0 likes · 20 min read

How Joint Optimization of Tree-Based Indexes Boosts Large-Scale Recommendation Accuracy

DataFunTalk

Oct 9, 2019 · Artificial Intelligence

Multilingual Content Understanding in UC International Feed Recommendation

This article presents a comprehensive overview of the challenges, requirements, and technical solutions for multilingual content understanding in UC's international information‑flow recommendation system, covering structured signal construction, low‑resource NLP techniques, transfer learning, quality modeling, and image‑based signal integration.

NLPRecommendation Systemscontent understanding

0 likes · 14 min read

Multilingual Content Understanding in UC International Feed Recommendation

DataFunTalk

Sep 29, 2019 · Artificial Intelligence

UC Information Flow Video Tag Recognition: System Architecture and Multi‑Modal Algorithms

This article presents a comprehensive overview of UC's information‑flow video tag recognition technology, detailing tag usage scenarios, the end‑to‑end system architecture, multi‑modal feature extraction, advanced deep‑learning models such as NextVlad, behavior and person tagging methods, and future research directions.

Computer VisionDeep LearningMultimodal Learning

0 likes · 14 min read

UC Information Flow Video Tag Recognition: System Architecture and Multi‑Modal Algorithms

DataFunTalk

Sep 27, 2019 · Artificial Intelligence

Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models

The talk by Meitu senior algorithm expert Chen Wenqiang details how deep‑learning‑driven embedding, recall, and ranking techniques—including Item2vec, twin‑tower DNNs, and multi‑task NFwFM—are applied to improve click‑through rates, follow conversions, and user engagement in Meitu's content community.

AIDeep LearningRecommendation Systems

0 likes · 3 min read

Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models

Snowball Engineer Team

Sep 4, 2019 · Artificial Intelligence

Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking

This article explores the evolution of recommendation algorithms at Xueqiu, highlighting the limitations of traditional point-wise click-through rate prediction models and detailing the ongoing transition to a pair-wise TF-Ranking framework designed to mitigate user and content biases while significantly enhancing overall recommendation accuracy and user experience.

Algorithm OptimizationCTR predictionPair-Wise Learning

0 likes · 5 min read

Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking

DataFunTalk

Aug 28, 2019 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

Recommendation systems, driven by recent economic and deep‑learning advances, face critical issues such as the lack of unified industrial benchmarks, limited explainability for users and content providers, and feedback‑loop induced data confounding, prompting calls for open datasets, transparent models, and collaborative optimization across stakeholders.

AIBenchmarkFeedback Loop

0 likes · 15 min read

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

Huajiao Technology

Aug 27, 2019 · Artificial Intelligence

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

This article provides a comprehensive technical overview of collaborative filtering, covering traditional user‑ and item‑based similarity methods, matrix‑factorization approaches for implicit feedback, various loss functions, and a suite of deep neural network models such as GMF, MLP, NeuMF, DMF, and ConvMF, together with implementation details, evaluation metrics, and practical deployment considerations.

Deep LearningRecommendation SystemsSpark

0 likes · 29 min read

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

Alibaba Cloud Developer

Aug 16, 2019 · Artificial Intelligence

How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems

IntentGC, a KDD 2019 paper, introduces a scalable graph convolution framework that fuses explicit user‑item interactions with rich heterogeneous signals to tackle link‑prediction on billion‑node e‑commerce graphs, offering efficient training, dual‑convolution design, and superior performance over existing baselines.

IntentGCRecommendation Systemsgraph convolution

0 likes · 10 min read

How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems

DataFunTalk

Aug 16, 2019 · Artificial Intelligence

Tree‑based Deep Match (TDM): Design, Implementation, and Applications in Large‑Scale Retrieval

This article presents a comprehensive overview of the Tree‑based Deep Match (TDM) algorithm, describing the evolution of retrieval technology, the limitations of traditional Match‑Rank pipelines, the design of a one‑stage tree‑indexed deep matching model, its training methodology, performance gains on public datasets, and its deployment in Alibaba’s advertising and e‑commerce platforms.

Recommendation SystemsTDMlarge scale

0 likes · 23 min read

Tree‑based Deep Match (TDM): Design, Implementation, and Applications in Large‑Scale Retrieval

DataFunTalk

Aug 14, 2019 · Artificial Intelligence

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

The article explores how the rapid growth of the internet has created information overload, discusses the challenges of recommendation systems such as sparsity and timeliness, outlines a four‑step personalized content pipeline, and highlights the interdisciplinary nature of building effective AI‑driven recommendation solutions.

AIBig DataRecommendation Systems

0 likes · 16 min read

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

Alibaba Cloud Developer

Aug 14, 2019 · Artificial Intelligence

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

This article presents a co-designed algorithm‑system solution—MIMN and an independent UIC module—that enables ultra‑long user behavior modeling for click‑through rate prediction, delivering significant offline AUC gains and online CTR/RPM improvements in Alibaba's display advertising platform.

CTR predictionDeep LearningRecommendation Systems

0 likes · 12 min read

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

dbaplus Community

Jul 27, 2019 · Fundamentals

How a Chinese Drama Illustrates Data Structures, Algorithms, and Time Complexity

The article uses the plot of the historical series “The Longest Day in Chang’an” to explain how proper use of data structures, recommendation algorithms, and time‑complexity optimizations—such as O(n²) brute‑force search, O(n) mapping, and O(log n) spatial tricks—can turn a desperate race against time into a successful mission, while also touching on big‑data analysis and simple encryption via the tower‑signal system.

AlgorithmsRecommendation Systemsencryption

0 likes · 4 min read

How a Chinese Drama Illustrates Data Structures, Algorithms, and Time Complexity

DataFunTalk

Jul 3, 2019 · Artificial Intelligence

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization

The article explains how recommendation systems balance exploitation and exploration, introduces diversity metrics such as temporal, spatial, and coverage, and presents a determinantal point process (DPP) based algorithm accelerated by Cholesky decomposition and greedy inference, demonstrating significant speedups and improved relevance‑diversity trade‑offs in experiments.

DiversityRecommendation Systemscholesky decomposition

0 likes · 10 min read

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization

DataFunTalk

Jul 1, 2019 · Artificial Intelligence

Data-Driven Foundations for Building Recommendation Systems

The article explains how data serves as a critical asset for recommendation systems, outlining the necessary steps from understanding business problems and data dimensions to collection, cleaning, integration, and analysis, while distinguishing explicit and implicit user feedback and emphasizing data quality, timeliness, and relevance.

Data QualityETLRecommendation Systems

0 likes · 11 min read

Data-Driven Foundations for Building Recommendation Systems

JD Retail Technology

Jun 15, 2019 · Artificial Intelligence

Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations

The department’s extensive 6.18 preparation involved systematic load‑testing, deep learning‑driven personalization of search recommendations, and multiple algorithmic enhancements to improve relevance and conversion, supported by detailed planning, cross‑team coordination, and dedicated night‑shift logistics.

AIAlgorithm OptimizationRecommendation Systems

0 likes · 6 min read

Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations

DataFunTalk

May 20, 2019 · Artificial Intelligence

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

The article reviews the characteristics of e‑commerce personalized prediction, outlines Alibaba's model iteration from large‑scale linear regression to deep learning architectures such as DIN, CrossMedia, and Deep Interest Evolution, and discusses future directions like disentangled representation and white‑box modeling.

Attention MechanismCTR predictionRecommendation Systems

0 likes · 11 min read

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

DataFunTalk

Apr 25, 2019 · Artificial Intelligence

Comparison of Classification and Ranking Models in Recommendation Systems

This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.

Recommendation Systemsclassification modelloss function

0 likes · 10 min read

Comparison of Classification and Ranking Models in Recommendation Systems

Sohu Tech Products

Apr 17, 2019 · Artificial Intelligence

CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks

This article reviews the evolution of click‑through‑rate (CTR) estimation models for recommendation ranking, covering logistic regression, feature‑engineering tricks, factorization machines, deep neural networks, wide‑and‑deep architectures, and the Deep & Cross Network, while discussing their strengths, limitations, and future research directions.

CTRDeep LearningRecommendation Systems

0 likes · 14 min read

CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks

Hulu Beijing

Apr 10, 2019 · Artificial Intelligence

Designing Deep Learning Models for Item Similarity in Recommendation Systems

This article explains how to build both unsupervised and supervised deep‑learning models that compute item similarity from user behavior, covering prod2vec embeddings, skip‑gram architectures, loss function design, and practical training steps for modern recommender systems.

Deep LearningRecommendation SystemsUnsupervised Learning

0 likes · 8 min read

Designing Deep Learning Models for Item Similarity in Recommendation Systems

Youku Technology

Apr 2, 2019 · Artificial Intelligence

How Youku Uses Multimodal AI for Video Understanding, Search, and Recommendation

Youku’s Algorithm Center has built a multimodal AI pipeline that jointly processes visual, audio, and textual signals to enhance video search, recommendation, and digital asset management, overcoming traditional keyword limits, improving relevance and cold‑start issues, while tackling fusion, cost, and interpretability challenges.

Multimodal AIRecommendation Systemscontent understanding

0 likes · 15 min read

How Youku Uses Multimodal AI for Video Understanding, Search, and Recommendation

DataFunTalk

Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecommendation Systems

0 likes · 51 min read

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

网易UEDC

Feb 25, 2019 · Product Management

How to Design Effective Content Distribution for Platforms: A LOFTER Case Study

This article examines the core challenges of content distribution on LOFTER, outlines a universal distribution framework based on content, channels, and users, analyzes content organization structures, production controls, value assessment, and channel strategies, and proposes improvements for LOFTER's ecosystem.

Content DistributionLOFTERProduct Design

0 likes · 12 min read

How to Design Effective Content Distribution for Platforms: A LOFTER Case Study

DataFunTalk

Feb 20, 2019 · Artificial Intelligence

Recommendation Reasoning and Its Path Toward Future AI

This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.

Future AIRecommendation Systemscausal reasoning

0 likes · 25 min read

Recommendation Reasoning and Its Path Toward Future AI

DataFunTalk

Feb 13, 2019 · Artificial Intelligence

Reinforcement Learning: Principles, Applications, and the PARL Framework

This comprehensive article explains reinforcement learning fundamentals, compares it with supervised learning, surveys Baidu's industrial RL applications such as recommendation, dialogue, prosthetics, and autonomous driving, introduces the open‑source PARL platform, and discusses current challenges and future research directions.

AIDialogue SystemsPARL

0 likes · 18 min read

Reinforcement Learning: Principles, Applications, and the PARL Framework

Alibaba Cloud Developer

Feb 13, 2019 · Artificial Intelligence

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

This article explores how cognitive computing combined with graph neural networks and text generation enables large‑scale interest mining, interpretable embeddings, and multi‑modal recommendation in e‑commerce, outlining platform implementations, explainable methods, and future directions for AI‑driven consumer engagement.

E-commerce AIRecommendation Systemscognitive computing

0 likes · 9 min read

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

DataFunTalk

Jan 28, 2019 · Artificial Intelligence

Deep Interest Evolution Network (DIEN): Modeling User Interest Evolution for Click‑Through Rate Prediction

This article introduces the Deep Interest Evolution Network (DIEN), an advanced deep learning model that extracts and evolves user interests over time to improve click‑through rate prediction for display advertising, detailing its background, architecture, auxiliary loss, attention‑augmented GRU, and both offline and online performance gains.

AdvertisingDIENRecommendation Systems

0 likes · 15 min read

Deep Interest Evolution Network (DIEN): Modeling User Interest Evolution for Click‑Through Rate Prediction

Alibaba Cloud Developer

Jan 9, 2019 · Artificial Intelligence

Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models

This article introduces core deep‑learning architectures—including MLP, RNN, CNN, auto‑encoders, and RBM—explains common activation and loss functions, and then surveys fourteen influential deep‑learning‑based recommendation algorithms such as FM, wide&deep, deepFM, NCF, GBDT+LR, seq2seq and YouTube DNN, complete with model diagrams and reference links.

AIDeep LearningNeural Networks

0 likes · 18 min read

Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models

Alibaba Cloud Developer

Jan 8, 2019 · Artificial Intelligence

Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained

This article surveys ten classic recommendation system algorithms—including collaborative filtering, association rules, Bayesian methods, K‑Nearest Neighbors, decision trees, random forests, matrix factorization, neural networks, word2vec, and logistic regression—detailing their principles, mathematical formulas, and practical implementation steps for real‑world applications.

Recommendation Systemsassociation rulescollaborative filtering

0 likes · 25 min read

Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained

DataFunTalk

Jan 3, 2019 · Artificial Intelligence

Machine Learning and Recommendation System Practice

This article presents a comprehensive overview of applying machine learning to recommendation systems, covering fundamental challenges such as user cold‑start, precise interest modeling, collaborative filtering, and both offline and online evaluation methods, while illustrating concepts with numerous diagrams.

AIRecommendation Systemscold start

0 likes · 9 min read

Machine Learning and Recommendation System Practice

Alibaba Cloud Developer

Dec 28, 2018 · Artificial Intelligence

Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%

This article describes how Ant Financial’s AI team redesigned TensorFlow to enable elastic feature scaling, introduced a Group‑Lasso optimizer and streaming frequency filtering, compressed models by 90%, and achieved significant CTR and efficiency gains in Alipay’s online recommendation system.

Online LearningRecommendation SystemsTensorFlow

0 likes · 20 min read

Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%

Alibaba Cloud Developer

Dec 18, 2018 · Artificial Intelligence

How Graph Embedding Boosts Cross-Category Bundle Recommendations on E‑Commerce

This article explains how graph embedding techniques, including a BSP‑based distributed LINE implementation and a cross‑category probabilistic graph model, are applied to improve the diversity and relevance of bundle (凑单) recommendations during large‑scale shopping events.

BSPLINE algorithmRecommendation Systems

0 likes · 14 min read

How Graph Embedding Boosts Cross-Category Bundle Recommendations on E‑Commerce

Beike Product & Technology

Dec 6, 2018 · Artificial Intelligence

Recommendation Systems in Real Estate: Practices and Insights from Lianke (Beike) at ArchSummit 2018

This article announces the ArchSummit 2018 global architect summit in Beijing, featuring a talk by Lianke (Beike) recommendation platform expert Xu Yansong on practical recommendation systems in real estate, covering architecture upgrades, algorithm iteration, and lessons learned.

Algorithm IterationArchSummit 2018Recommendation Systems

0 likes · 4 min read

Recommendation Systems in Real Estate: Practices and Insights from Lianke (Beike) at ArchSummit 2018

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

0 likes · 15 min read

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

Alibaba Cloud Developer

Dec 4, 2018 · Artificial Intelligence

Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%

This article presents a comprehensive set of innovations—including elastic feature scaling, a Group Lasso optimizer, streaming frequency filtering, and graph‑cut model compression—that transform TensorFlow for large‑scale online learning, delivering significant CTR gains and up to 90% model size reduction in Alibaba's recommendation systems.

Online LearningRecommendation Systemsfeature engineering

0 likes · 19 min read

Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%

Programmer DD

Nov 21, 2018 · Artificial Intelligence

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

A Java developer shares his self‑learning journey into AI, recounts a technical interview covering recommendation system types, TF‑IDF similarity metrics, word2vec behavior modeling, and SVM fundamentals, and reflects on the challenges and resources that helped him transition into algorithm engineering.

AIRecommendation Systemsinterview

0 likes · 7 min read

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

iQIYI Technical Product Team

Nov 2, 2018 · Artificial Intelligence

iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications

The third iQIYI Tech Salon in Beijing, held despite strong winds, showcased five expert talks on AI‑driven video library management, NLP for entertainment content, AI‑based video encoding, traffic anti‑fraud systems, and short‑video personalized recommendation, illustrating AI’s impact on content creation, quality, security, and user experience.

AINLPRecommendation Systems

0 likes · 5 min read

iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications

Hulu Beijing

Oct 12, 2018 · Artificial Intelligence

How Hulu Boosted Recommendation Diversity with Determinantal Point Processes

This article explains how Hulu tackled the trade‑off between accuracy and diversity in its massive video recommendation system by applying Determinantal Point Processes and an efficient incremental greedy algorithm, achieving 100× speed‑ups without sacrificing recommendation quality.

DiversityHuluRecommendation Systems

0 likes · 7 min read

How Hulu Boosted Recommendation Diversity with Determinantal Point Processes

DataFunTalk

Oct 12, 2018 · Artificial Intelligence

Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms

The article presents a comprehensive overview of Ele.me's food‑delivery recommendation system, detailing its business model, platform goals, unique challenges, market‑driven efficiency mechanisms, control strategies, system architecture, model evolution, and online‑learning techniques used to balance short‑term performance with long‑term ecosystem health.

AIEle.meOnline Learning

0 likes · 15 min read

Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms

21CTO

Sep 24, 2018 · Artificial Intelligence

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

This article explains the fundamentals of recommendation algorithms, illustrates their modest impact with real‑world examples, and outlines how modern e‑commerce systems collect data, rank items, and use rapid A/B testing to continuously improve personalized recommendations.

A/B testingRecommendation Systemsalgorithm design

0 likes · 10 min read

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

Alibaba Cloud Developer

Aug 29, 2018 · Artificial Intelligence

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

An in‑depth look at Alibaba’s billion‑scale graph embedding framework—GES and EGES—reveals how side‑information‑enhanced embeddings address user long‑tail coverage and cold‑start challenges, improving recommendation diversity and discovery across massive e‑commerce datasets and enabling real‑time personalized ranking.

Recommendation Systemscold starte‑commerce

0 likes · 7 min read

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

AntTech

Aug 22, 2018 · Artificial Intelligence

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing

The article presents three Ant Financial research papers featured at KDD 2018—one on graph‑learning fraud detection for return‑freight insurance, another introducing the adaptive GeniePath graph neural network, and a third describing a distributed collaborative hashing system for large‑scale recommendation—highlighting their methodologies, experimental results, and practical impact on Ant Financial’s services.

Ant FinancialHashingRecommendation Systems

0 likes · 21 min read

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing