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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 2023Cold Start
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.

Cold StartRankingRecommendation systems
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.

AIRecommendation systemsevaluation metrics
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.

Intelligent Reachdecision optimizatione-commerce
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.

AICold StartIntent Prediction
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.

Recommendation systemsdatasetsmachine learning
0 likes · 8 min read
Selected Papers from CIKM 2022 on Real‑Time Short Video Recommendation and Large‑Scale Datasets
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.

Cold StartTransformerembedding 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.

AIBias MitigationKuaishou
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.

Cold StartRecommendation systemsdeep learning
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.

CTR predictionNetEase Cloud Musicadvertising
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
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.

Cold StartTravelgraph neural network
0 likes · 20 min read
Personalized Marketing Platform for Travel: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
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 DataMicroservicesdata pipeline
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.

Bias MitigationContent MiningRecall Model
0 likes · 12 min read
Content Mining and Recall Model Practices in the Quanmin K Song Recommendation System
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.

Real-time Featureslarge-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.

AlibabaCTR predictionattention mechanism
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.

Cold StartTravelgraph neural network
0 likes · 22 min read
Personalized Marketing Platform for Travel Scenarios: Architecture, Algorithms, and Cold‑Start Solutions at Alibaba Fliggy
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.

WeChat AIcontent recalldeep learning
0 likes · 32 min read
WeChat "Look" Content Recall Architecture and Deep Learning Techniques
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.

long-term rewardoff-policyrecommender systems
0 likes · 13 min read
Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers