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User Modeling

41 articles · Page 1 of 1
James' Growth Diary
James' Growth Diary
Jun 6, 2026 · Artificial Intelligence

How Honcho’s Dialectic User Model Lets Agents Learn Your Preferences Over Time

The article explains how Honcho transforms scattered conversation facts into a structured user model through a dialectic reasoning loop, detailing memory vs. user model differences, tool architecture, recall modes, prefetch caching, cost‑control mechanisms, peer cards, and common pitfalls for building ever‑more personalized AI agents.

AgentCost ControlDialectic Reasoning
0 likes · 15 min read
How Honcho’s Dialectic User Model Lets Agents Learn Your Preferences Over Time
Machine Heart
Machine Heart
Apr 6, 2026 · Artificial Intelligence

Introducing LifeSim: The First Long‑Horizon User Life Simulator Redefining Personalized LLM Evaluation

LifeSim introduces a long‑horizon user life simulation framework that jointly models user cognition via a BDI engine and external environment, enabling realistic evaluation of personalized LLM assistants through the LifeSim‑Eval benchmark, which reveals current models excel at explicit intents but struggle with hidden intents and long‑term user understanding.

BDI modelLLM evaluationLifeSim
0 likes · 9 min read
Introducing LifeSim: The First Long‑Horizon User Life Simulator Redefining Personalized LLM Evaluation
DataFunSummit
DataFunSummit
Nov 22, 2025 · Artificial Intelligence

Breaking the Recommendation Filter Bubble: Alibaba 1688’s Inference‑Driven AI

Alibaba’s 1688 platform leverages inference‑based large language models to enhance recommendation discovery, addressing the filter‑bubble problem by analyzing long‑term buyer behavior, compressing extensive activity streams, generating nuanced demand queries, and integrating multimodal data and market trend agents to deliver more diverse, explainable product suggestions for B‑type buyers.

AIE‑commerceRecommendation Systems
0 likes · 23 min read
Breaking the Recommendation Filter Bubble: Alibaba 1688’s Inference‑Driven AI
DataFunSummit
DataFunSummit
Sep 16, 2025 · Artificial Intelligence

How Human‑Centric Evaluation Transforms Conversational Recommender Systems

This article reviews Professor Jin Yucheng’s research on conversational recommender systems, detailing a human‑centric evaluation framework, the CRS‑Que assessment model, and ChatGPT‑based experiments that reveal how dialogue quality, user trust, and prompt design jointly shape system performance.

AIUser Modelingconversational recommender systems
0 likes · 16 min read
How Human‑Centric Evaluation Transforms Conversational Recommender Systems
Meituan Technology Team
Meituan Technology Team
Aug 15, 2024 · Artificial Intelligence

Meituan's Exploration and Practice in Advertising Algorithm: Information Flow Ad Estimation

This article details Meituan Waimai's feed advertising system, covering business characteristics, the evolution of estimation models, and practical implementations such as decision‑path modeling, ultra‑long/wide user modeling, full‑reconstruction techniques, and the integration of large language models for CTR prediction.

CTR estimationLLMMeituan
0 likes · 22 min read
Meituan's Exploration and Practice in Advertising Algorithm: Information Flow Ad Estimation
NewBeeNLP
NewBeeNLP
Jul 22, 2024 · Artificial Intelligence

How Meta Scales User Modeling for Ads: Inside the SUM Framework

This article examines Meta's SUM (Scaling User Modeling) system, detailing its upstream‑downstream architecture, the SOAP online asynchronous serving platform, production optimizations, and extensive offline and online experiments that demonstrate significant gains in ad personalization performance.

MetaRecommendation SystemsUser Modeling
0 likes · 19 min read
How Meta Scales User Modeling for Ads: Inside the SUM Framework
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Nov 6, 2023 · Artificial Intelligence

Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions

At CNCC 2023, leading researchers and industry experts convened to examine how large language models can transform recommendation systems, outlining four core challenges—model integration, fluency versus intelligence, hallucination versus deception, and user understanding—while highlighting opportunities such as multimodal content, cold‑start solutions, zero‑shot ranking, instruction‑driven algorithms, and responsible, interactive recommendation pipelines.

AICNCC 2023LLM applications
0 likes · 16 min read
Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions
DataFunSummit
DataFunSummit
Oct 4, 2023 · Artificial Intelligence

Comprehensive Overview of Recommendation System Technologies and Their Evolution

This article provides a detailed overview of modern recommendation system technology, covering system architecture, user understanding layers, various recall and ranking techniques, additional algorithmic directions such as cold‑start and bias modeling, and the evolving evaluation metrics used in practice.

Recommendation SystemsUser Modelingbias modeling
0 likes · 14 min read
Comprehensive Overview of Recommendation System Technologies and Their Evolution
DataFunTalk
DataFunTalk
May 8, 2023 · Artificial Intelligence

Comprehensive Overview of Modern Recommendation System Technologies

This article presents a detailed survey of recent advances in recommendation system technology, covering system architecture, user understanding layers, various recall methods, ranking techniques, auxiliary algorithms such as cold-start and bias modeling, and evaluation metrics, with references to industry practices and academic research.

AIEvaluation MetricsRecommendation Systems
0 likes · 13 min read
Comprehensive Overview of Modern Recommendation System Technologies
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Apr 10, 2023 · Artificial Intelligence

Intelligent Reach System: Modeling, Decision Making, and Optimization for E‑commerce

The paper presents an intelligent reach system for e‑commerce that automatically selects audience, timing, channel, welfare and creative content using user, content and decision models—including XGBoost churn predictions, NLP‑generated copy, Bayesian CTR estimation and linear‑programming optimization—resulting in a 17.4 % rise in paying users and a 5 % revenue boost over manual methods.

Decision OptimizationIntelligent ReachUser Modeling
0 likes · 20 min read
Intelligent Reach System: Modeling, Decision Making, and Optimization for E‑commerce
DataFunTalk
DataFunTalk
Apr 1, 2023 · Artificial Intelligence

Real-Time User Understanding Service (RTUS) for Travel: Architecture, Algorithms, and Experimental Evaluation

This article presents the design and implementation of the Real‑Time User Understanding Service (RTUS) for the Fliggy travel platform, detailing its architecture, multi‑chain data fusion, model and data reuse techniques, and several AI‑driven algorithms for cold‑start interest representation, intent prediction, and destination forecasting, together with extensive offline and online experimental results.

AIIntent PredictionTravel Industry
0 likes · 21 min read
Real-Time User Understanding Service (RTUS) for Travel: Architecture, Algorithms, and Experimental Evaluation
Kuaishou Tech
Kuaishou Tech
Aug 31, 2022 · Artificial Intelligence

Selected Papers from CIKM 2022 on Real‑Time Short Video Recommendation and Large‑Scale Datasets

This article summarizes four CIKM 2022 papers that present a client‑side short‑video recommender, the fully‑observed KuaiRec dataset, the unbiased KuaiRand sequential recommendation dataset, and an industrial‑scale solution for billion‑user lifetime value prediction, highlighting their motivations, methods, and reported impacts.

User Modelingdatasetsshort video
0 likes · 8 min read
Selected Papers from CIKM 2022 on Real‑Time Short Video Recommendation and Large‑Scale Datasets
Hulu Beijing
Hulu Beijing
Aug 19, 2022 · Artificial Intelligence

Disney’s M5 Model: Multi‑Modal, Multi‑Interest, Multi‑Scenario Boost for Streaming Recommendations

Disney’s Content Discovery team introduces M5, a multi‑modal, multi‑interest, multi‑scenario recall model that enhances VOD and live streaming recommendations by leveraging rich metadata, user behavior, and contextual features, outperforming baseline methods with significant hit‑ratio gains across Hulu and Disney+.

M5 modelMulti-modalRecommendation Systems
0 likes · 22 min read
Disney’s M5 Model: Multi‑Modal, Multi‑Interest, Multi‑Scenario Boost for Streaming Recommendations
Alimama Tech
Alimama Tech
Jul 20, 2022 · Artificial Intelligence

Cold-Transformer: Embedding Adaptation for User Cold‑Start Recommendation

Cold‑Transformer tackles the user cold‑start problem by introducing an Embedding Adaption layer that refines sparse user embeddings using context‑aware fused behavior sequences and a label‑encoding scheme, preserving a dual‑tower design and achieving state‑of‑the‑art performance on public and industrial datasets.

User Modelingcold-startembedding adaptation
0 likes · 21 min read
Cold-Transformer: Embedding Adaptation for User Cold‑Start Recommendation
DataFunSummit
DataFunSummit
May 26, 2022 · Artificial Intelligence

Exploring Contrastive Learning in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how contrastive learning can alleviate data sparsity and distribution bias in recommendation systems, detailing its theoretical advantages, recent research progress in computer vision and NLP, and a multi‑task self‑supervised framework applied to Kuaishou's short‑video ranking pipeline with significant offline and online performance gains.

AIKuaishouRecommendation Systems
0 likes · 21 min read
Exploring Contrastive Learning in Kuaishou Recommendation Systems
DaTaobao Tech
DaTaobao Tech
Apr 6, 2022 · Artificial Intelligence

Improving New User Experience in Taobao Live Recommendation via Multi‑Channel Lifelong Product Sequence Modeling

The paper tackles Taobao Live’s cold‑start problem for new users by introducing a multi‑channel lifelong product‑sequence network that enriches purchase histories with side information, extracts relevance‑focused subsequences across five channels, and integrates them via target‑attention DIN, achieving substantial offline and online performance gains, especially for low‑activity users.

MultimodalRecommendation SystemsUser Modeling
0 likes · 23 min read
Improving New User Experience in Taobao Live Recommendation via Multi‑Channel Lifelong Product Sequence Modeling
DataFunTalk
DataFunTalk
Mar 12, 2022 · Artificial Intelligence

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

This article presents a comprehensive overview of NetEase Cloud Music's advertising system, detailing its architecture, core challenges, CTR and CVR prediction models, feature engineering, model evolution from LR to deep learning, user vector modeling, and practical recommendations for improving ad performance.

AdvertisingCTR PredictionNetEase Cloud Music
0 likes · 15 min read
NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution
Kuaishou Tech
Kuaishou Tech
Oct 12, 2021 · Artificial Intelligence

Concept‑aware Denoising Graph Neural Network (CONDE) for Short Video Recommendation

CONDE, a concept‑aware denoising graph neural network proposed by Wuhan University and Kuaishou, leverages heterogeneous three‑part graphs, attention‑based graph convolutions, and Gumbel‑Softmax‑driven edge sampling to filter noisy user‑video interactions, achieving up to 6 % AUC improvement on short‑video and e‑commerce recommendation tasks.

AIDenoisingGraph Neural Network
0 likes · 10 min read
Concept‑aware Denoising Graph Neural Network (CONDE) for Short Video Recommendation
21CTO
21CTO
Sep 7, 2021 · Artificial Intelligence

Designing Effective Short‑Video Recommendation Systems: Goals, Multi‑Objective Modeling, and Long‑Term Value

This article examines the rapid growth of short‑video platforms, outlines the core problems a recommendation system must solve for users, creators, and advertisers, describes the end‑to‑end pipeline, explores multi‑objective modeling and fusion techniques, and discusses long‑term value estimation for sustained user engagement.

AIUser Modelinglong-term value
0 likes · 8 min read
Designing Effective Short‑Video Recommendation Systems: Goals, Multi‑Objective Modeling, and Long‑Term Value
DataFunSummit
DataFunSummit
Sep 3, 2021 · Artificial Intelligence

Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its multi‑scene architecture, user‑session modeling, graph‑based recommendation algorithms, cold‑start strategies, cross‑domain user mapping, and a hierarchical travel‑play tag system that together enable large‑scale, real‑time, thousand‑person‑one‑face marketing.

Graph Neural NetworkTravelUser Modeling
0 likes · 20 min read
Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Apr 22, 2021 · Big Data

Inside Toutiao’s Massive Big Data & Recommendation Architecture

This article examines Toutiao’s rapid growth from a small startup to a platform serving over 500 million users, detailing its data collection, user modeling, cold‑start handling, recommendation engines, storage solutions, messaging push system, micro‑service design, and virtualized PaaS infrastructure that enable high‑throughput, personalized news delivery.

Cloud ComputingMicroservicesUser Modeling
0 likes · 9 min read
Inside Toutiao’s Massive Big Data & Recommendation Architecture
Top Architect
Top Architect
Apr 9, 2021 · Big Data

Technical Architecture and Data Processing of Toutiao News Feed System

This article provides a comprehensive overview of Toutiao's rapid growth, massive user base, data collection pipelines, user modeling, recommendation engine, storage solutions, message push strategies, micro‑service architecture, and virtualization PaaS platform, illustrating how big‑data technologies enable personalized news delivery at scale.

Big DataMicroservicesToutiao
0 likes · 8 min read
Technical Architecture and Data Processing of Toutiao News Feed System
DataFunTalk
DataFunTalk
Apr 1, 2021 · Artificial Intelligence

Content Mining and Recall Model Practices in the Quanmin K Song Recommendation System

This talk explains how Quanmin K Song extracts high‑quality user‑generated content, designs multi‑stage recall pipelines—including attribute‑based, model‑based, and other recall methods—and applies iterative model improvements, negative‑sampling strategies, and bias‑mitigation techniques to enhance recommendation performance.

Content MiningRecall ModelUser Modeling
0 likes · 12 min read
Content Mining and Recall Model Practices in the Quanmin K Song Recommendation System
Code Ape Tech Column
Code Ape Tech Column
Dec 1, 2020 · Information Security

Why Calling Everyone a “User” Is a Hidden Security Risk

The article explains how the vague term “user” creates design flaws and security vulnerabilities across domains such as airline booking systems, Unix environments, and SaaS platforms, and argues for precise terminology to avoid costly rework and confused‑deputy attacks.

Access ControlTerminologyUser Modeling
0 likes · 7 min read
Why Calling Everyone a “User” Is a Hidden Security Risk
Tencent Cloud Developer
Tencent Cloud Developer
Sep 30, 2020 · Artificial Intelligence

Tencent Kankan Information Feed: Architecture, Challenges, and Optimizations

Peng Mo’s talk details Tencent Kankan’s billion‑user feed architecture—layered data, recall, ranking, and exposure control—while addressing real‑time feature generation, massive concurrency, memory‑intensive caching, and fast indexing, and explains solutions such as multi‑level caches, online minute‑level model updates, Redis bloom‑filter exposure filtering, a lock‑free hash‑plus‑linked‑list index, and distributed optimizations that halve latency to under 500 ms and support auto‑scaling and cold‑start handling.

User Modelinglarge-scale architectureonline learning
0 likes · 15 min read
Tencent Kankan Information Feed: Architecture, Challenges, and Optimizations
DataFunTalk
DataFunTalk
Aug 29, 2020 · Artificial Intelligence

User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

This article presents Alibaba's comprehensive approach to user modeling for search CTR/CVR ranking, detailing the abstraction of user information, multi‑scale behavior processing, enhanced transformer‑based model structures, client‑side click and exposure modeling, and experimental results showing significant AUC improvements.

AlibabaAttention MechanismCTR Prediction
0 likes · 18 min read
User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba
DataFunTalk
DataFunTalk
Aug 3, 2020 · Artificial Intelligence

Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy

This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its architecture, scenario and functional abstractions, user‑modeling pipelines, full‑stack traffic control, cold‑start techniques, cross‑domain mapping, heterogeneous graph modeling, and a hierarchical travel‑play tag system to achieve thousand‑person‑one‑face recommendation across daily and promotional scenes.

Graph Neural NetworkTravelUser Modeling
0 likes · 22 min read
Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
Liangxu Linux
Liangxu Linux
Aug 2, 2020 · Information Security

Why the Word “User” Is a Hidden Security Risk in Software Design

The article explains how the vague term “user” leads to design flaws and security vulnerabilities across systems like ticket‑booking platforms, Unix, and SaaS, and argues that precise terminology and early conceptual planning can prevent costly rework.

Access ControlUser Modelingbest practices
0 likes · 8 min read
Why the Word “User” Is a Hidden Security Risk in Software Design
DataFunTalk
DataFunTalk
Jul 17, 2020 · Artificial Intelligence

WeChat "Look" Content Recall Architecture and Deep Learning Techniques

This article details the technical architecture behind WeChat's "Look" content recall, covering content sourcing, profiling, multimodal tagging, knowledge‑graph representations, propensity and target detection, multi‑stage recall pipelines, and a range of deep learning models including sequence, translation, BERT, dual‑tower, hybrid, and graph neural network approaches.

Graph Neural NetworkUser ModelingWeChat AI
0 likes · 32 min read
WeChat "Look" Content Recall Architecture and Deep Learning Techniques
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 7, 2020 · Artificial Intelligence

How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention

This article details Alibaba’s Taobao search CTR/CVR user modeling approach, covering background, model architecture with self‑attention and attention pooling, handling short‑term, long‑term, and on‑device behavior sequences, experimental results showing AUC improvements, and future directions.

CTR PredictionSelf-AttentionUser Modeling
0 likes · 20 min read
How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention
21CTO
21CTO
Oct 6, 2019 · Artificial Intelligence

How Toutiao’s AI Recommendation Engine Works: From Content Analysis to Real‑Time Ranking

This article explains the architecture and principles of Toutiao’s recommendation system, covering its three‑dimensional model of content, user and environment features, content analysis techniques, user tagging, real‑time training pipelines, evaluation methods, and content safety measures that together drive personalized feeds.

Real-time TrainingUser Modelingcontent analysis
0 likes · 18 min read
How Toutiao’s AI Recommendation Engine Works: From Content Analysis to Real‑Time Ranking
DataFunTalk
DataFunTalk
Sep 30, 2019 · Artificial Intelligence

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

This article reviews recent advances in applying reinforcement learning to recommendation systems, explains the fundamental RL concepts, discusses the specific challenges such as large action spaces, bias, and long‑term reward modeling, and summarizes two influential YouTube papers along with practical insights and future directions.

Off-PolicyUser Modelinglong-term reward
0 likes · 13 min read
Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers
AntTech
AntTech
Jun 10, 2019 · Artificial Intelligence

Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems

This article presents a model‑based reinforcement learning framework for recommendation systems that uses a generative adversarial user model to simultaneously learn user behavior dynamics and reward functions, enabling efficient Cascading‑DQN policy learning and achieving superior long‑term user rewards and click‑through rates in experiments.

Cascading DQNGenerative Adversarial NetworksUser Modeling
0 likes · 9 min read
Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems
21CTO
21CTO
May 31, 2019 · Information Security

Why Using the Word “User” Can Sabotage Your Software Design

The article explains how the vague term “User” leads to poor requirements, hidden security flaws like the Confused Deputy problem, and costly redesigns, urging developers to adopt precise terminology such as “team” and “member” early in a project.

Access ControlTerminologyUser Modeling
0 likes · 8 min read
Why Using the Word “User” Can Sabotage Your Software Design
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 14, 2018 · Artificial Intelligence

Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations

This paper proposes a novel attention‑based framework that groups and encodes heterogeneous user behavior sequences into separate semantic subspaces, applies self‑attention to capture inter‑behavior influences, and demonstrates faster training and comparable or improved recommendation performance across multiple tasks and datasets.

Multi-Task LearningSelf-AttentionUser Modeling
0 likes · 12 min read
Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations
21CTO
21CTO
Jan 3, 2016 · Artificial Intelligence

How Meilishuo Personalizes Fashion: Inside Its AI‑Driven Recommendation Engine

This article explores how Meilishuo, China’s leading fast‑fashion discovery platform, tackles fragmented mobile attention by using AI‑powered personalization techniques—including user modeling, real‑time feedback, and tailored push notifications—to deliver highly relevant fashion recommendations and boost user engagement.

AIUser Modelinge-commerce
0 likes · 6 min read
How Meilishuo Personalizes Fashion: Inside Its AI‑Driven Recommendation Engine