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Jun 16, 2024 · Artificial Intelligence

Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances

This article presents a comprehensive overview of applying reinforcement learning to recommendation systems, covering background challenges, practical exploration, frontier research directions, multi‑agent and inverse RL approaches, evaluation methods, and future outlooks, based on a KDD‑published study and industry experience.

Inverse RLRecommendation Systemsevaluation

0 likes · 24 min read

Reinforcement Learning in Recommendation Systems: Practice, Challenges, and Industry Advances

DataFunTalk

Jun 15, 2024 · Artificial Intelligence

DataFunSummit2024 Recommendation System Architecture Summit Overview

The DataFunSummit2024 Recommendation System Architecture Summit invites participants to explore cutting‑edge advances in large‑model recommendation, training and inference optimization, feature engineering, multi‑task modeling, and graph‑based techniques through a series of expert talks and panel discussions from leading industry and academic researchers.

AIRecommendation Systemsconference

0 likes · 33 min read

DataFunSummit2024 Recommendation System Architecture Summit Overview

JD Cloud Developers

Jun 13, 2024 · Artificial Intelligence

How LLMs Are Redefining Recommender Systems for JD Union Ads

This article surveys the impact of large language models on recommendation systems, outlines generative recommender architectures, discusses challenges of JD Union advertising, presents a semantic‑ID based solution with training and inference details, and reports offline and online experimental results.

AILLMRecommendation Systems

0 likes · 22 min read

How LLMs Are Redefining Recommender Systems for JD Union Ads

DataFunSummit

Jun 12, 2024 · Artificial Intelligence

Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions

This article reviews how large language models are transforming recommendation systems, covering their fundamentals, recent LLM‑enabled methods for representation, learning and generalization, challenges such as scalability, bias and privacy, and future research directions including personalized prompts and robust model integration.

LLMRecommendation Systemsmodel generalization

0 likes · 19 min read

Large Language Model (LLM) Powered Recommendation Systems: Overview, Techniques, Challenges, and Future Directions

NewBeeNLP

May 24, 2024 · Artificial Intelligence

How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning

This article reviews the NoteLLM paper, which leverages Llama 2 to create richer text embeddings and automatically generate tags and categories for note recommendation, addressing cold‑start issues through a multitask prompt design, generative‑contrastive learning, and collaborative supervised fine‑tuning, and demonstrates strong offline and online gains.

EmbeddingGenerative Contrastive LearningLLM

0 likes · 14 min read

How NoteLLM Boosts Cold‑Start Recommendation with Generative Contrastive Learning

DataFunTalk

May 9, 2024 · Artificial Intelligence

Graph Model Practices and Applications in Baidu Recommendation System

This article introduces the background of graph data, explains common graph modeling algorithms such as graph embedding and graph neural networks, compares their strengths, and details the evolution and large‑scale deployment of Feed graph models in Baidu's recommendation platform.

BaiduEmbeddingRecommendation Systems

0 likes · 11 min read

Graph Model Practices and Applications in Baidu Recommendation System

DataFunSummit

May 4, 2024 · Artificial Intelligence

Applications of Large Language Models in Recommendation Systems: Overview and Future Directions

This article provides a comprehensive overview of how large language models (LLMs) are integrated into recommendation systems, detailing two main paradigms—LLM as a component and LLM as a standalone system—while discussing their impact on retrieval, ranking, prompting, and outlining future research challenges such as multimodal recommendation, hallucination mitigation, bias reduction, and agent‑based approaches.

AIFuture DirectionsLLM

0 likes · 6 min read

Applications of Large Language Models in Recommendation Systems: Overview and Future Directions

JD Tech Talk

Apr 25, 2024 · Artificial Intelligence

Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms

This article reviews the evolution of JD’s recommendation advertising ranking mechanism, covering its economic auction origins, challenges of multi‑material valuation, user interest uncertainty, and multi‑item auction fairness, and describes AI‑driven solutions such as deep auction models and reinforcement‑learning‑based ListVCG.

Recommendation Systemsauctione‑commerce

0 likes · 19 min read

Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms

AntTech

Apr 17, 2024 · Artificial Intelligence

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

LLMRG introduces a novel framework that leverages large language models to construct personalized reasoning graphs, integrating chain reasoning, self‑verification, divergent extension, and knowledge‑base self‑improvement, thereby enhancing recommendation accuracy, interpretability, and performance across multiple benchmark datasets without additional user or item information.

AIInterpretabilityRecommendation Systems

0 likes · 9 min read

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

DataFunTalk

Apr 3, 2024 · Artificial Intelligence

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

This presentation outlines the current bottlenecks of conventional recommendation pipelines and proposes a 2026 roadmap that includes retention improvement, user‑growth strategies, content‑ecosystem metrics, Pareto‑optimal multi‑objective optimization, long‑term value modeling, site‑wide spatial optimization, interactive recommendation, personalized modeling, and the integration of large‑model fusion through the OneRec framework.

Recommendation SystemsUser Retentioninteractive recommendation

0 likes · 18 min read

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

DataFunTalk

Apr 2, 2024 · Artificial Intelligence

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

This article provides a comprehensive overview of user portrait algorithms, covering their historical development, ontology‑based traditional approaches, deep‑learning enhancements, representation‑learning techniques such as lookalike, active‑learning driven iteration, and the integration of large‑model world knowledge, while also discussing current challenges and future research directions.

Deep LearningOntologyRecommendation Systems

0 likes · 26 min read

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

DataFunSummit

Mar 29, 2024 · Artificial Intelligence

Large Language Model (LLM) Revolution in Recommendation Systems: Overview, Techniques, and Future Directions

This article reviews how the rapid rise of large language models, exemplified by ChatGPT, is transforming recommendation systems by addressing traditional ID‑centric limitations, introducing prompt‑based and ID‑free representations, discussing recent research advances, practical challenges, and future research directions.

AILLMRecommendation Systems

0 likes · 18 min read

Large Language Model (LLM) Revolution in Recommendation Systems: Overview, Techniques, and Future Directions

DataFunTalk

Mar 28, 2024 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario recommendation algorithms, detailing background challenges, algorithm classifications such as TAML, CausalInt, and DFFM, their modular designs, experimental validations, and practical Q&A insights for large‑scale advertising systems.

Recommendation Systemsadvertising algorithmsmachine learning

0 likes · 19 min read

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

NewBeeNLP

Mar 28, 2024 · Industry Insights

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

Meta introduces a generative recommendation framework (GR) built on the Hierarchical Sequential Transduction Unit (HSTU) that unifies heterogeneous features, treats user behavior as a new modality, and leverages novel encoder and inference optimizations to achieve order‑of‑magnitude scaling in model size, training compute, and online latency while delivering 12‑18% online gains over traditional deep recommendation models.

Generative ModelsHSTUMeta

0 likes · 36 min read

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

NewBeeNLP

Mar 15, 2024 · Industry Insights

How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering

Meta’s new Generative Recommendation (GR) paper replaces a decade‑old hierarchical feature paradigm with an ultra‑long sequence transformer that directly fuses user profiles, behaviors, and targets, offering stronger feature crossing, richer information utilization, and massive compute gains, while revealing scaling‑law effects in recommendation systems.

Generative ModelsMetaRecommendation Systems

0 likes · 9 min read

How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering

Ops Development & AI Practice

Mar 13, 2024 · Artificial Intelligence

How Vector Retrieval Powers AI Model Training and Real-World Applications

Vector retrieval, based on converting data into high‑dimensional vectors and measuring similarity, enables fast, accurate search across massive datasets, supporting AI tasks such as search engines, recommendation, NLP, and computer vision, and plays a crucial role in large‑model training for data selection, anomaly detection, and model optimization.

AI trainingRecommendation SystemsVector Retrieval

0 likes · 6 min read

How Vector Retrieval Powers AI Model Training and Real-World Applications

DaTaobao Tech

Feb 19, 2024 · Artificial Intelligence

AI/ML Technology Articles Collection

This collection compiles technical articles that explore diverse AI/ML applications, from deploying large language models on MacBooks and building e‑commerce recommendation engines, to leveraging the LangChain framework, creating AIGC‑driven fashion solutions, and implementing Stable Diffusion for image generation.

AIAIGCDeployment

0 likes · 1 min read

DataFunTalk

Feb 7, 2024 · Big Data

Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage

This article presents Kuaishou's end‑to‑end engineering solutions for handling massive, real‑time recommendation model data, covering scenario description, complex business pipelines, trillion‑parameter model storage, high‑throughput processing with Flink and NVM, and future directions for cloud‑native scalability.

KuaishouNVM storageRecommendation Systems

0 likes · 15 min read

Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage

DataFunSummit

Jan 23, 2024 · Artificial Intelligence

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

This article presents Tencent TRS's industrial practice of applying meta‑learning and cross‑domain recommendation to address personalization challenges, detailing problem definitions, solution architectures, algorithmic choices such as MAML, deployment strategies, and the cost‑effective outcomes achieved across multiple scenarios.

Industrial AIMAMLMeta Learning

0 likes · 16 min read

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

Kuaishou Tech

Jan 23, 2024 · Artificial Intelligence

Highlights of Five Selected AAAI 2024 Papers on Recommendation, Retrieval, and Video Generation

This article presents concise overviews of five AAAI 2024 accepted papers covering multi‑stage reinforcement‑learning recommendation, error‑adaptive watch‑time prediction, coarse‑to‑fine text‑to‑video retrieval, enhanced fashion image retrieval, and conditional image‑to‑video generation, each with authors, download links, and reported performance gains.

AAAI 2024Recommendation Systemsartificial intelligence

0 likes · 14 min read

Highlights of Five Selected AAAI 2024 Papers on Recommendation, Retrieval, and Video Generation

Model Perspective

Jan 22, 2024 · Artificial Intelligence

How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions

This article explains the principles of A/B testing and the ε‑greedy multi‑armed bandit algorithm, illustrates their practical use in e‑commerce recommendation optimization, and draws broader life lessons about balancing exploration and exploitation for better personal and professional decisions.

A/B testingRecommendation Systemsexploration vs exploitation

0 likes · 6 min read

How A/B Testing and the ε‑Greedy Multi‑Armed Bandit Can Boost Decisions

DataFunTalk

Jan 19, 2024 · Artificial Intelligence

Improving the MIND Multi‑Interest Recommendation Model with Capsule Initialization and Routing Enhancements

This article presents a comprehensive study of the MIND multi‑interest recommendation model, detailing its original architecture, identified shortcomings, and proposed enhancements—including capsule initialization via max‑min and Markov methods, routing simplifications, and training adjustments—along with experimental results and business impact assessments.

AIMINDRecommendation Systems

0 likes · 19 min read

Improving the MIND Multi‑Interest Recommendation Model with Capsule Initialization and Routing Enhancements

Sohu Tech Products

Jan 10, 2024 · Artificial Intelligence

Baidu's Practices and Insights on Recommendation Ranking

Baidu’s recommendation ranking system handles billions of daily impressions and millions of users by combining discrete and cross features, bias mitigation, and long‑short sequence modeling within a multi‑stage funnel and hierarchical architecture, while planning to integrate large language models for generative, interpretable, and decision‑oriented recommendations.

AIBaiduRecommendation Systems

0 likes · 19 min read

Baidu's Practices and Insights on Recommendation Ranking

DataFunTalk

Jan 7, 2024 · Artificial Intelligence

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

This article presents Baidu's comprehensive approach to feed recommendation ranking, covering business and data background, feature engineering principles, core algorithmic strategies, system architecture design, and upcoming plans to integrate large language models for more intelligent and fair recommendations.

BaiduRecommendation Systemsfeature engineering

0 likes · 19 min read

Baidu's Recommendation Ranking: Background, Feature Design, Algorithms, Architecture, and Future Directions

DataFunTalk

Jan 6, 2024 · Artificial Intelligence

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

This article presents Ant Group's causal debiasing techniques for recommendation and marketing, covering bias background, data‑fusion based MDI model, back‑door adjustment methods, experimental results on public and industry datasets, and practical applications in advertising and e‑commerce.

MarketingRecommendation Systemscausal inference

0 likes · 16 min read

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

DataFunSummit

Dec 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

This article presents a two‑stage constrained actor‑critic (TSCAC) algorithm that models short‑video recommendation as a constrained reinforcement‑learning problem, details its theoretical formulation and optimization loss, and validates its superiority through extensive offline and online experiments, followed by a multi‑task reinforcement‑learning framework (RMTL) that further improves multi‑objective recommendation performance.

Recommendation Systemsconstrained optimizationmulti-task learning

0 likes · 16 min read

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

DataFunSummit

Dec 22, 2023 · Artificial Intelligence

Cross‑Domain Multi‑Objective Estimation and Fusion in Baidu Video Recommendation: Design, Modeling, and System Evolution

This article shares Baidu's experience and thinking on cross‑domain multi‑objective estimation and fusion for video recommendation, covering background, system overview, multi‑objective design and modeling, long‑term value attribution, cross‑domain network architecture, and the evolution‑strategy based fusion approach.

BaiduRecommendation Systemscross-domain

0 likes · 13 min read

Cross‑Domain Multi‑Objective Estimation and Fusion in Baidu Video Recommendation: Design, Modeling, and System Evolution

Alibaba Cloud Developer

Dec 20, 2023 · Artificial Intelligence

Turning Complexity into Results: Practical Strategies for Recommendation Engineers

This article explores why recommendation engineers often struggle to deliver measurable outcomes, examining system complexity, uncertainty, delayed feedback, and personal belief, and then offers concrete principles and actionable approaches to prioritize work, align with business goals, and achieve tangible results.

AIRecommendation Systemsalgorithm engineering

0 likes · 11 min read

Turning Complexity into Results: Practical Strategies for Recommendation Engineers

Architect

Dec 14, 2023 · Artificial Intelligence

How Multi‑Task Multi‑Scene Modeling Powers ZhiZhuan’s Search: Algorithms, Industry Practices, and Lessons

This article analyzes the challenges of multi‑task and multi‑scene recommendation for large‑scale C‑end services, reviews key academic and industry solutions such as Shared‑Bottom, MMoE, PLE, ESMM, LHUC, PEPNet, MTMS and HiNet, and details ZhiZhuan’s end‑to‑end architecture that achieved over 6% click‑through and 2% conversion improvements.

AI model architectureRecommendation SystemsZhiZhuan

0 likes · 15 min read

How Multi‑Task Multi‑Scene Modeling Powers ZhiZhuan’s Search: Algorithms, Industry Practices, and Lessons

DataFunTalk

Dec 12, 2023 · Artificial Intelligence

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.

Recommendation Systemsdata leakagelarge language models

0 likes · 20 min read

Challenges and Considerations of Recommendation Systems: Evaluation, Data Leakage, and the Role of Large Models

Sohu Tech Products

Dec 6, 2023 · Artificial Intelligence

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.

AlibabaReal-time ControlRecommendation Systems

0 likes · 34 min read

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

DataFunSummit

Dec 5, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

This article presents a comprehensive study of a scenario‑adaptive and self‑supervised multi‑scenario recommendation model (SASS) for Taobao, detailing its motivation, adaptive multi‑scenario architecture, two‑stage pre‑training and fine‑tuning, experimental validation, deployment in the recall stage, and practical challenges addressed through Q&A.

AlibabaRecommendation Systemsmulti‑scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

DataFunSummit

Dec 2, 2023 · Artificial Intelligence

OPPO’s Unified Modeling Strategy for App Distribution: Balancing Cost Reduction and User Value

In this interview, OPPO’s senior manager Lai Hongke explains how the company tackles the challenges of sparse, cross‑scenario data in app distribution by deploying a unified modeling framework, MMOE sharing, and the oCPX capability to simultaneously cut costs, improve recommendation performance, and preserve user value across its software store and game center.

AIOPPORecommendation Systems

0 likes · 11 min read

OPPO’s Unified Modeling Strategy for App Distribution: Balancing Cost Reduction and User Value

DataFunTalk

Dec 2, 2023 · Artificial Intelligence

OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value

The article examines how OPPO tackles the challenges of sparse, multi‑scenario app‑distribution data by deploying a unified modeling framework, leveraging MMoe and oCPX techniques to enhance recommendation performance, reduce costs, and preserve user value across its software store and game center.

OPPORecommendation Systemsdata sparsity

0 likes · 11 min read

OPPO's Unified Modeling for App Distribution: Balancing Cost Reduction and User Value

DataFunTalk

Nov 29, 2023 · Artificial Intelligence

Cross-Domain Multi-Objective Estimation and Fusion in Baidu Video Recommendation

This article presents Baidu's technical experience on designing, estimating, and fusing cross-domain multi-objective models for its immersive video recommendation system, covering business background, system architecture, target design, long‑term value modeling, and evolution strategies.

AIRecommendation Systemscross-domain

0 likes · 14 min read

Cross-Domain Multi-Objective Estimation and Fusion in Baidu Video Recommendation

DataFunTalk

Nov 27, 2023 · Artificial Intelligence

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

This article introduces STAN, a user‑lifecycle‑aware multi‑task recommendation model proposed by Shopee that refines CTR, CVR, and stay‑time predictions by identifying and tracking user states, demonstrates offline gains on Shopee and public datasets, and reports online improvements in click‑through, dwell‑time, and order metrics.

CTRCVRRecommendation Systems

0 likes · 8 min read

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

360 Smart Cloud

Nov 20, 2023 · Artificial Intelligence

Overview of Recent Open‑Source AI Models and Tools (November 2023)

This article summarizes a collection of newly released open‑source AI projects covering natural‑language processing, multimodal processing, intelligent agents, recommendation systems, and model training acceleration, providing brief descriptions, key capabilities, and links to their repositories.

AIRecommendation Systemslarge language models

0 likes · 9 min read

Overview of Recent Open‑Source AI Models and Tools (November 2023)

Practical DevOps Architecture

Nov 20, 2023 · Backend Development

Comprehensive Python Full-Stack Development Course Outline (28 Chapters)

This article presents a detailed 28‑chapter curriculum for mastering Python full‑stack development, covering Linux basics, Python fundamentals, web front‑end design with Vue, RESTful API creation with Flask, Django and Django REST Framework, big‑data processing with Hadoop, Spark and MapReduce, feature engineering, recommendation systems, and live streaming system implementation.

BackendBig DataFull-Stack Development

0 likes · 3 min read

Comprehensive Python Full-Stack Development Course Outline (28 Chapters)

Alimama Tech

Nov 15, 2023 · Artificial Intelligence

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.

Recommendation Systemsad rankingcontrastive learning

0 likes · 16 min read

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

DataFunTalk

Nov 14, 2023 · Artificial Intelligence

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

This article presents a comprehensive study of a controllable multi‑objective re‑ranking model for Taobao's information‑flow recommendation, detailing the challenges of complex feed scenarios, three modeling paradigms (V1‑V3), an actor‑critic reinforcement learning framework with hypernet‑generated weights, and extensive online evaluation results.

Real-time ControlRecommendation Systemshypernetworks

0 likes · 31 min read

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

Sohu Tech Products

Nov 8, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

The presentation introduces a two‑stage constrained actor‑critic algorithm that learns auxiliary policies for interaction signals before optimizing watch‑time under KL constraints, and a reinforcement‑learning multi‑task learning framework that models session‑level dynamics with adaptive multi‑critic weighting, both achieving significant offline and online gains in short‑video recommendation.

Recommendation Systemsactor-criticconstrained optimization

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

Zhuanzhuan Tech

Nov 7, 2023 · Artificial Intelligence

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

This article outlines the challenges of multi-task and multi-scenario modeling for large-scale C-end services, reviews key industry approaches such as Shared-Bottom, MMoE, PLE, ESMM, and LHUC, and details Zhaozhuan’s own EPNET-based solution that improved click-through and conversion rates.

CTRCVRRecommendation Systems

0 likes · 13 min read

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

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

DataFunTalk

Nov 6, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

This article presents a two‑stage constrained actor‑critic reinforcement learning algorithm for short‑video recommendation, models the problem as a constrained MDP, details the algorithm’s stages, and reports extensive offline and online experiments showing superior watch‑time and interaction metrics, followed by a multi‑task RL framework and its evaluations.

Recommendation Systemsconstrained optimizationmulti‑task learning

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

DataFunTalk

Nov 3, 2023 · Product Management

Strategy Product Management: Principles, Frameworks, and Q&A for Content Recommendation

This article explains the role and mindset of a strategy product manager, outlines the decision‑making framework for content recommendation platforms, compares it with related positions, and answers practical questions about value, AI impact, commercial‑consumer trade‑offs, and content creation versus consumption.

AI ImpactRecommendation Systemscontent management

0 likes · 16 min read

Strategy Product Management: Principles, Frameworks, and Q&A for Content Recommendation

DataFunTalk

Oct 31, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.

AIGCRecommendation SystemsUplift Modeling

0 likes · 22 min read

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

DataFunSummit

Oct 23, 2023 · Artificial Intelligence

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

This article examines how large language models fit into recommendation systems by discussing problem definitions, offline evaluation pitfalls such as data leakage, dataset construction issues exemplified by MovieLens, and the practical limits of using LLMs as a universal solution.

MovieLensRecommendation Systemsdata leakage

0 likes · 18 min read

Large Models in Recommendation Systems: Evaluation Challenges, Data Leakage, and Practical Considerations

DataFunSummit

Oct 14, 2023 · Artificial Intelligence

Career Planning for Algorithm Engineers: Stages, Strategies, and Skill Development

This article outlines the three key career stages for algorithm engineers, offers practical planning advice through vision, self‑evaluation and action, and discusses industry trends, skill‑building paths, mindset, and work‑life balance to help engineers navigate a volatile tech landscape.

AICareer DevelopmentRecommendation Systems

0 likes · 29 min read

Career Planning for Algorithm Engineers: Stages, Strategies, and Skill Development

DataFunSummit

Oct 12, 2023 · Artificial Intelligence

Graph Models in Baidu's Recommendation System: Foundations, Algorithms, and Feed Model Evolution

This article explains how graph models are applied in Baidu's recommendation system, covering graph basics, common graph algorithms such as graph embeddings and GNNs, and the evolution and deployment of the Feed graph model across multiple product lines.

BaiduRecommendation Systemsgraph embeddings

0 likes · 10 min read

Graph Models in Baidu's Recommendation System: Foundations, Algorithms, and Feed Model Evolution

DataFunSummit

Oct 9, 2023 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario algorithms applied to recommendation systems, covering background challenges, algorithm taxonomy, recent research, detailed model architectures such as TAML, CausalInt and DFFM, experimental results on public and private datasets, and a Q&A discussion.

AdvertisingRecommendation Systemsmachine learning

0 likes · 20 min read

DataFunSummit

Oct 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.

FairnessRecommendation Systemscausal inference

0 likes · 9 min read

Fairness in Recommendation Systems: Consumer and Provider Perspectives

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 Systemsbias modelingrecall

0 likes · 14 min read

Comprehensive Overview of Recommendation System Technologies and Their Evolution

DataFunTalk

Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIRecommendation Systemsbias mitigation

0 likes · 3 min read

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

Alimama Tech

Sep 20, 2023 · Artificial Intelligence

CCF C³ Forum: AI Technology Driving Business Transformation

The 23rd CCF C³ Forum, organized by Alibaba’s Alimama and the CCF CTO Club, examined how large‑model AI is reshaping intelligent business technology, from data‑driven to knowledge‑driven approaches, enhancing e‑commerce with smarter search, personalized recommendations, content creation, and guiding merchants on future AI‑native strategies.

AI technologyAI-native businessData Intelligence

0 likes · 8 min read

CCF C³ Forum: AI Technology Driving Business Transformation

DaTaobao Tech

Sep 13, 2023 · Artificial Intelligence

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

The article surveys how large language models can be integrated into recommendation systems, either as feature extractors or as end‑to‑end recommenders, showing that LLM‑derived semantics improve recall, ranking, diversity, and user experience, and outlining future multimodal, efficiency, and re‑ranking directions.

EmbeddingLLMPrompt engineering

0 likes · 19 min read

Integrating Large Language Models with Recommendation Systems: Paradigms, Methods, and Experiments

DataFunSummit

Sep 1, 2023 · Artificial Intelligence

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.

Recommendation Systemscausal inferencedeconfounding

0 likes · 22 min read

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

Xiaohongshu Tech REDtech

Aug 25, 2023 · Artificial Intelligence

DataFunSummit 2023: Recommendation Systems Online Summit

The DataFunSummit 2023 online summit (August 26‑27) will explore eight recommendation‑system topics—including core and engineering architecture, model training/inference, large models, graphs, cold start, and multi‑task scenarios—featuring Xiaohongshu leaders who will present on graph‑based business architecture, integrated training‑inference pipelines, and user/content cold‑start strategies.

AI EngineeringRecommendation Systemsarchitecture

0 likes · 6 min read

DataFunSummit 2023: Recommendation Systems Online Summit

NetEase LeiHuo UX Big Data Technology

Aug 23, 2023 · Artificial Intelligence

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

This article explains the principles of collaborative filtering, outlines its three main types—user‑based, item‑based, and model‑based—and focuses on model‑based approaches such as matrix factorization, clustering, and deep‑learning techniques for recommending personalized game items to improve player experience and monetization.

Model-BasedRecommendation Systemsartificial intelligence

0 likes · 7 min read

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

Ele.me Technology

Aug 22, 2023 · Artificial Intelligence

Multi-Granularity Attention Model for Group Recommendation (MGAM)

The Multi‑Granularity Attention Model (MGAM) improves group recommendation by extracting subset, group, and superset preferences through hierarchical attention and graph neural networks, fusing them via self‑attention, and achieves state‑of‑the‑art offline results and a 1.2% online CTR lift in Alibaba’s local‑life services.

AIDeep LearningRecommendation Systems

0 likes · 18 min read

Multi-Granularity Attention Model for Group Recommendation (MGAM)

DataFunTalk

Aug 21, 2023 · Artificial Intelligence

Can We Build Large-Scale Models for Recommendation Systems?

In this talk, Zhang Pengtao, a Sina Weibo technical expert with a Ph.D. in computer applications, explores how the strong memory capabilities of NLP large language models inspire the design of independent memory mechanisms for recommendation systems, covering model concepts, HCNet & MemoNet, experimental results, and practical takeaways for enhancing recommendation model performance.

AIMemory MechanismsRecommendation Systems

0 likes · 2 min read

Can We Build Large-Scale Models for Recommendation Systems?

Ele.me Technology

Aug 17, 2023 · Artificial Intelligence

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

BASM is a bottom‑up adaptive spatiotemporal model for online food ordering that uses hierarchical embedding, semantic transformation, and adaptive bias layers to dynamically modulate parameters according to time and location, thereby capturing multiple data distributions and achieving superior offline metrics and online A/B test performance.

CTR predictionRecommendation Systemsadaptive parameters

0 likes · 18 min read

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

Ele.me Technology

Aug 16, 2023 · Artificial Intelligence

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

The paper introduces StEN, a spatiotemporal-enhanced network for CTR prediction in location-based services, combining static spatiotemporal feature activation, dynamic preference activation, and target attention, achieving state-of-the-art offline results and a 1.6% CTR lift in online tests.

Deep LearningRecommendation Systemsclick-through rate

0 likes · 19 min read

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

Meituan Technology Team

Aug 10, 2023 · Artificial Intelligence

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

The article showcases seven Meituan research papers accepted at KDD 2023—spanning feed‑stream, cross‑domain, takeaway, bonus allocation, contour‑based segmentation, living‑needs prediction, and multilingual recommendation—detailing their novel methods, real‑world deployments, and concluding with an invitation for academic collaboration.

KDD 2023MeituanRecommendation Systems

0 likes · 17 min read

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

DataFunTalk

Aug 7, 2023 · Artificial Intelligence

DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview

The DataFun Decision Intelligence Summit brings together leading researchers and industry experts to present cutting‑edge reinforcement learning algorithms, safety considerations, distributional methods, and real‑world applications such as vehicle routing, recommender systems, and power‑grid scheduling, highlighting future directions and audience takeaways.

AIRecommendation Systemsdistributional RL

0 likes · 12 min read

DataFun Decision Intelligence Summit – Reinforcement Learning Forum Overview

DataFunSummit

Aug 5, 2023 · Big Data

Manbang Group's Real-Time Computing, Data Architecture, and Product Practices

Manbang Group shares its practical experiences and insights on real-time computing, multi‑cloud platform architecture, data warehousing with Flink and Holo, real‑time decision and feature platforms, and future plans for scaling these systems to support logistics and recommendation algorithms.

Cloud NativeData ArchitectureFlink

0 likes · 16 min read

Manbang Group's Real-Time Computing, Data Architecture, and Product Practices

Bitu Technology

Aug 2, 2023 · Artificial Intelligence

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

This article details Tubi's multi‑stage recommendation system, focusing on the recall phase and describing how popularity metrics, embedding averaging, per‑video nearest‑neighbors, hierarchical clustering, real‑time ranking, and context‑aware sampling are combined to efficiently generate personalized video candidates at scale.

EmbeddingRecommendation SystemsVideo Streaming

0 likes · 10 min read

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

DataFunTalk

Jul 18, 2023 · Artificial Intelligence

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

This article presents Fliggy's work on user travel demand prediction, outlining the unique challenges of travel scenarios, the evolution of recall and ranking algorithms—including multi‑task learning, graph‑based models, and intention‑capture mechanisms—and discusses future research directions such as long‑sequence modeling and cross‑domain learning.

Recommendation Systemsgraph neural networksmachine learning

0 likes · 19 min read

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

DataFunTalk

Jul 16, 2023 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article introduces graph neural networks, explains graph representation learning, discusses their evolution from random walks to spectral and spatial convolutions, and details how OPPO applies GNNs to improve recommendation system recall and ranking, highlighting practical architecture, experimental gains, and future research directions.

OPPORecommendation Systemsgraph neural networks

0 likes · 19 min read

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

DataFunTalk

Jul 6, 2023 · Artificial Intelligence

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

This article presents Tencent TRS's industrial deployment of meta‑learning and cross‑domain recommendation, detailing problem definitions, solution architectures, challenges of industrialization, and practical implementations that achieve personalized modeling and cost‑effective multi‑scene recommendation across various online services.

Industrial AIMAMLRecommendation Systems

0 likes · 18 min read

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

DataFunSummit

Jul 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, causal embedding techniques, experimental results on MovieLens and Yelp, and future research directions.

FairnessMetricsRecommendation Systems

0 likes · 9 min read

DataFunSummit

Jun 30, 2023 · Artificial Intelligence

Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions

In this expert roundtable, leading researchers and engineers discuss the current state of recommendation systems, how large language models can reshape the field, the technical and practical challenges involved, and practical advice for practitioners looking to adopt AI‑driven personalization solutions.

AIRecommendation Systemsdialogue recommendation

0 likes · 36 min read

Roundtable on Large‑Model‑Based Recommendation Systems: Opportunities, Challenges, and Future Directions

DataFunTalk

Jun 20, 2023 · Artificial Intelligence

How Recommendation Systems Work and Their Integration with ChatGPT

This article explains the fundamentals of recommendation systems, their digital representation, how ChatGPT and large language models are applied to enhance recommendation performance, and highlights emerging trends such as conversational recommendation and a recommended book on the subject.

AIChatGPTConversational AI

0 likes · 8 min read

How Recommendation Systems Work and Their Integration with ChatGPT

58 Tech

May 26, 2023 · Artificial Intelligence

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

The A2M Summit announcement details a 2023 AI and machine learning conference where senior algorithm architect Liu Lixi presents his talk on practical recommendation system techniques for sparse data, low‑frequency scenarios, and ad‑creative optimization within 58.com’s industry‑wide digital transformation.

58.comAIIndustrial Transformation

0 likes · 5 min read

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

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

DaTaobao Tech

Apr 28, 2023 · Artificial Intelligence

Multi-Scenario Recommendation Model

The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.

AIDeep LearningRecommendation Systems

0 likes · 23 min read

Kuaishou Tech

Apr 28, 2023 · Artificial Intelligence

How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems

This article presents the Hyper‑Actor Critic (HAC) framework that splits reinforcement‑learning policies into continuous hyper‑actions and effective recommendation lists, introduces alignment and supervised losses, and demonstrates superior performance on an online simulator compared to existing RL and supervised methods.

AI researchRecommendation Systemshyper-actor critic

0 likes · 9 min read

How Hyper‑Actor Critic Redefines Reinforcement Learning for Recommendation Systems

DataFunTalk

Apr 24, 2023 · Artificial Intelligence

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

This article reviews the evolution of Weibo's large‑scale recommendation technology, covering the system's business scenarios, technical roadmap, recent large model iterations, multi‑task and multi‑scenario modeling, feature engineering, consistency between recall and ranking, and emerging techniques such as causal inference and graph methods.

Recommendation Systemscausal inferencegraph embeddings

0 likes · 18 min read

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

Kuaishou Tech

Apr 23, 2023 · Artificial Intelligence

Kuaishou & Renmin AI Institute: Driving Multimodal Large Model Innovation

The article details how Kuaishou’s multimodal AI research, including its K7 trillion‑parameter model and VLUA algorithm, partners with Renmin University’s Gaoling AI Institute to launch a joint lab, produce cutting‑edge papers such as WebBrain and ChatImg, and advance recommendation and search technologies across the short‑video ecosystem.

AIIndustry collaborationRecommendation Systems

0 likes · 17 min read

Kuaishou & Renmin AI Institute: Driving Multimodal Large Model Innovation

DataFunTalk

Apr 22, 2023 · Artificial Intelligence

Career Planning for Algorithm Engineers: Stages, Strategies, and Technical Development

This article provides algorithm engineers with a comprehensive career roadmap covering three key professional stages, practical planning advice, technical skill sharpening, industry trend analysis, and a detailed Q&A to help navigate the volatile tech job market.

AICareer DevelopmentRecommendation Systems

0 likes · 28 min read

Career Planning for Algorithm Engineers: Stages, Strategies, and Technical Development

DataFunSummit

Apr 14, 2023 · Big Data

An Overview of User Profiling: Definitions, Elements, Types, Dimensions, Applications, and Development Process

This article provides a comprehensive introduction to user profiling, covering its definition, key elements, classification types, common dimensions, practical application scenarios, lifecycle considerations, development workflow, and validation methods for building effective data‑driven user models.

Big DataMarketingRecommendation Systems

0 likes · 10 min read

An Overview of User Profiling: Definitions, Elements, Types, Dimensions, Applications, and Development Process

DataFunTalk

Apr 10, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

This article presents a comprehensive study of multi‑scenario personalized recommendation, introducing a scenario‑adaptive and self‑supervised model (SASS) that jointly addresses data sparsity, domain adaptation, and recall‑stage deployment through a two‑stage training pipeline and extensive experiments on Alibaba’s Taobao platform.

AlibabaRecommendation Systemsmulti‑scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

Xiaohongshu Tech REDtech

Apr 3, 2023 · Industry Insights

What Drives Intelligent Recommendation and Search? Key Takeaways from Xiaohongshu’s CCF C³ Event

The CCF C³ event at Xiaohongshu gathered leading researchers and industry experts to dissect the latest advances, challenges, and future opportunities in intelligent recommendation and search, including multimodal content handling, decentralized distribution, cold‑start solutions, and the impact of large language models.

AIRecommendation SystemsSearch

0 likes · 11 min read

What Drives Intelligent Recommendation and Search? Key Takeaways from Xiaohongshu’s CCF C³ Event

Kuaishou Large Model

Mar 31, 2023 · Artificial Intelligence

How Kuaishou Elevates Video Quality and AI Performance at NVIDIA GTC 2023

At NVIDIA GTC 2023, Kuaishou engineers unveiled cutting‑edge solutions ranging from video quality assessment and enhancement, 3D digital‑human live streaming, a custom TensorRT‑based performance framework, large‑scale recommendation model acceleration, to multimodal massive‑model deployment for short‑video scenarios.

AI OptimizationDigital HumanRecommendation Systems

0 likes · 9 min read

How Kuaishou Elevates Video Quality and AI Performance at NVIDIA GTC 2023

DaTaobao Tech

Mar 24, 2023 · Artificial Intelligence

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

In a March 27 livestream, Alibaba senior algorithm engineer Chen Zhihong will explain how popularity bias affects recommendation pipelines, review existing mitigation techniques, and introduce a decoupled domain‑adaptive unbiased dual‑tower recall model that leverages bias while preserving recommendation fairness.

Recommendation SystemsUnbiased Recallmachine learning

0 likes · 2 min read

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

Alibaba Cloud Infrastructure

Mar 22, 2023 · Artificial Intelligence

CUTLASS Extreme Performance Optimization and Its Application in Alibaba's Recommendation System

At the GTC conference, the talk presents Alibaba Cloud’s heterogeneous computing platform and introduces the Open Deep Learning API (ODLA), then details how CUTLASS‑based operator fusion dramatically accelerates attention and MLP layers in large‑scale recommendation models, achieving multi‑fold performance gains in production.

CUTLASSDeep LearningGPU computing

0 likes · 5 min read

CUTLASS Extreme Performance Optimization and Its Application in Alibaba's Recommendation System

Huawei Cloud Developer Alliance

Mar 18, 2023 · Artificial Intelligence

Unveiling NetEase’s ‘YuZhi’ Multimodal Model: Boosting Personalized Recommendations

NetEase’s Fuxi team developed the multimodal ‘YuZhi’ model, a large‑scale image‑text dual‑tower system optimized with the EET inference framework, which powers personalized recommendations in NetEase News and Cloud Music, while a partnership with Huawei Ascend AI and MindSpore enables further model acceleration, compression, and the new ‘YuZhi‑Wukong’ model that improves video recommendation metrics by about 5%.

Huawei Ascend AILarge ModelMindSpore

0 likes · 5 min read

Unveiling NetEase’s ‘YuZhi’ Multimodal Model: Boosting Personalized Recommendations

JD Cloud Developers

Feb 27, 2023 · Artificial Intelligence

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

The article explains JD’s Explore & Exploit (EE) module, its bias‑related challenges, the iterative optimization loop, model debiasing techniques for position and popularity bias, personalized bias modeling, causal inference methods, online AB results, and offline evaluation metrics, highlighting significant improvements in search diversity and efficiency.

EE moduleRecommendation Systemsbias mitigation

0 likes · 16 min read

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

DaTaobao Tech

Feb 13, 2023 · Artificial Intelligence

Why Recommendation Systems Matter: From Basics to Advanced Strategies

This article explains what recommendation systems are, their core tasks, evaluation metrics, popular algorithms such as collaborative filtering and latent factor models, how to handle cold‑start and contextual challenges, the role of social networks, and typical system architecture, providing a comprehensive overview for beginners and practitioners.

Evaluation MetricsRecommendation Systemscold start

0 likes · 21 min read

Why Recommendation Systems Matter: From Basics to Advanced Strategies

DataFunTalk

Feb 5, 2023 · Artificial Intelligence

A Six‑Year Retrospective on Deep Learning Algorithms and Their Applications

This article reviews the author’s six‑year hands‑on experience with deep learning, covering breakthroughs in speech recognition, computer vision, language modeling, reinforcement learning, privacy protection, model compression, recommendation systems, and future research directions, while summarizing technical lessons and practical insights.

AIRecommendation Systemsmodel compression

0 likes · 30 min read

A Six‑Year Retrospective on Deep Learning Algorithms and Their Applications

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

This article presents a comprehensive overview of the background, current multi‑path recall methods, and a series of practical optimizations—including dual‑tower models, enhanced vectors, an unbiased IPW‑based framework, and a travel‑state‑aware deep recall model—applied to Feizhu's homepage recommendation system, with both offline and online experimental results demonstrating click‑through rate improvements.

Recommendation Systemsbias mitigationdual-tower model

0 likes · 17 min read

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Between Heaven and Earth: Reflections of an Algorithm Engineer

The article argues that algorithm engineers should move beyond a narrow focus on deep‑learning models, emphasizing the importance of system architecture, data quality, and thoughtful problem framing to break through performance plateaus in advertising and recommendation systems.

AdvertisingData QualityRecommendation Systems

0 likes · 10 min read

Between Heaven and Earth: Reflections of an Algorithm Engineer

DataFunTalk

Jan 21, 2023 · Artificial Intelligence

Challenges and Best Practices in Recommendation Systems – Expert Interview

This interview with three recommendation‑system experts explores the technical architecture, data sources, feature engineering, recall and ranking strategies, evaluation metrics, cold‑start solutions, and practical difficulties, offering actionable insights to avoid common pitfalls in real‑world recommender deployments.

Evaluation MetricsRecommendation Systemscold start

0 likes · 15 min read

Challenges and Best Practices in Recommendation Systems – Expert Interview

Bilibili Tech

Dec 20, 2022 · Industry Insights

Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study

This article presents a detailed study on applying recommendation‑system techniques to test case prioritization for Bilibili's mobile apps, describing the problem definition, evaluation metrics, data processing, FM model selection, experimental results, practical deployment, and future research directions.

BilibiliRecommendation SystemsSoftware Testing

0 likes · 13 min read

Can Recommendation Algorithms Speed Up Test Case Prioritization? A Bilibili Case Study

Alibaba Cloud Big Data AI Platform

Dec 15, 2022 · Artificial Intelligence

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%

Vivo’s AI recommendation team leveraged Alibaba’s DeepRec engine—introducing dynamic Embedding Variables, feature admission/elimination, Parquet datasets, and advanced CPU/GPU inference optimizations such as SessionGroup, device placement, multi‑stream and BladeDISC compilation—resulting in notable gains in model accuracy, latency reduction, and resource efficiency.

DeepRecGPU inferenceRecommendation Systems

0 likes · 13 min read

Vivo’s DeepRec: Dynamic Embedding and GPU Tricks that Raised CTR by 1.2%

HomeTech

Dec 9, 2022 · Artificial Intelligence

Interview with Li Benyang: AI, Knowledge Graphs, and Career Insights in Intelligent Recommendation

In this interview, Li Benyang, head of the intelligent recommendation content understanding team at Autohome, shares his AI background, the evolution of recommendation systems, knowledge‑graph construction for automotive content, career choices between big firms and startups, and practical advice for technologists navigating fast‑changing industries.

AIKnowledge GraphRecommendation Systems

0 likes · 12 min read

Interview with Li Benyang: AI, Knowledge Graphs, and Career Insights in Intelligent Recommendation

NetEase LeiHuo UX Big Data Technology

Nov 22, 2022 · Artificial Intelligence

Sample Weighting in Machine Learning: From YouTube Playback Duration to Game Recommendation Optimization

This article explains why and how sample weighting is used in machine learning, illustrates YouTube's conversion of video watch time into sample weights to align with its commercial goals, and describes practical weighted‑logistic‑regression techniques applied to improve game recommendation systems.

AIRecommendation SystemsYouTube

0 likes · 8 min read

Sample Weighting in Machine Learning: From YouTube Playback Duration to Game Recommendation Optimization

DataFunSummit

Nov 3, 2022 · Artificial Intelligence

Applying NVIDIA MPS to Boost GPU Utilization for Recommendation Inference

This article explains why traditional CPU inference and naïve GPU usage are inefficient for recommendation workloads, introduces NVIDIA Multi‑Process Service (MPS) technology, describes VIVO's custom Rust‑based inference engine and deployment strategies, and presents performance and cost benefits along with practical deployment considerations.

GPU inferenceKubernetesMPS

0 likes · 13 min read

Applying NVIDIA MPS to Boost GPU Utilization for Recommendation Inference

DataFunTalk

Nov 1, 2022 · Artificial Intelligence

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

This article presents the practical evolution of NetEase Yanxuan's recommendation pipeline, covering background, multi‑objective and cross‑domain modeling, bias correction, loss function enhancements, long‑term value strategies, and multi‑scene modeling, with experimental results and a Q&A session.

AIBias CorrectionMMoE

0 likes · 20 min read

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

Alimama Tech

Oct 19, 2022 · Artificial Intelligence

Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models

The study reveals that industrial deep click‑through‑rate models often overfit dramatically after the first training epoch—a “one‑epoch phenomenon” caused by the embedding‑plus‑MLP architecture, fast optimizers, and highly sparse features, with performance dropping sharply unless sparsity is reduced or training is limited to a single pass.

CTREmbeddingMLP

0 likes · 15 min read

Understanding the One-Epoch Overfitting Phenomenon in Deep Click-Through Rate Models

Model Perspective

Oct 14, 2022 · Artificial Intelligence

How SimRank Leverages Graph Theory for Powerful Recommendations

SimRank, a graph‑theoretic recommendation algorithm, models users and items as a bipartite graph and computes similarity through iterative matrix operations, with extensions like SimRank++ incorporating edge weights and evidence, while scalable solutions use big‑data frameworks or Monte‑Carlo simulations.

Big DataMatrix ComputationRecommendation Systems

0 likes · 8 min read

How SimRank Leverages Graph Theory for Powerful Recommendations