This article presents a curated list of cutting‑edge resources covering multimodal GraphRAG, knowledge‑graph‑driven large‑model applications in finance, healthcare, automotive, and more, offering insights into the evolving synergy between AI and knowledge graphs.
This resource compiles a curated list of cutting‑edge studies and frameworks that combine multimodal GraphRAG, knowledge‑graph techniques, and large‑model AI across sectors such as finance, healthcare, and manufacturing, guiding readers through emerging paradigms and future directions.
This article explains how GraphRAG combines knowledge graphs with retrieval‑augmented generation to overcome the limitations of vector‑only RAG, delivering higher accuracy, better explainability, easier development, and stronger governance for generative AI applications across various domains.
This article demonstrates how to construct a knowledge graph from a 58,099‑character document using GraphRAG, measures the indexing time on an M2 PRO machine, and compares Global, Local, and Drift query modes, highlighting three scenarios where Local mode is unsuitable.
This article presents a curated catalog of cutting‑edge AI resources, covering multimodal GraphRAG, knowledge‑graph and large‑model integration, financial industry AI products, Chinese‑medicine decision support, AI‑driven knowledge‑graph evolution, private‑domain Q&A pipelines, and emerging trends and standards, with a QR code for the full ebook.
This article presents a comprehensive overview of cutting‑edge research on integrating large language models with knowledge graphs, covering multimodal GraphRAG, financial AI solutions, traditional Chinese medicine decision support, and industry‑specific knowledge services, guiding readers through emerging paradigms and practical implementations.
This article walks through the complete GraphRAG workflow—from environment setup and indexing to command‑line and Python API queries—demonstrating how to build a knowledge graph, tune prompts, and retrieve high‑quality answers using Microsoft’s GraphRAG implementation.
This article examines how Retrieval‑Augmented Generation (RAG) has progressed from simple keyword‑based retrieval to advanced semantic methods, modular architectures, graph‑enhanced reasoning, and autonomous agentic systems, highlighting each approach's workflow, benefits, limitations, and the shift toward dynamic AI decision‑making.
This article walks through GraphRAG’s retrieval phase, showing how knowledge‑graph entities, relationships, and community reports are assembled into a query context, comparing local and global modes with traditional RAG, and illustrating the process with a concrete “Age of Big Data” example.
This article walks through the complete GraphRAG knowledge‑graph building pipeline—text splitting, entity extraction, relation mining, community clustering, and report generation—using a concrete example from the book “The Age of Big Data,” and explains why each step improves retrieval and answer quality.
This article explains GraphRAG’s architecture, compares it with traditional RAG, and presents experimental results showing that GraphRAG’s knowledge‑graph‑driven retrieval markedly improves answer accuracy, especially on low‑match, multi‑paragraph queries.
This article explores how a testing team can abstract core testing capabilities, construct a unified testing ecosystem, and integrate AI at strategic points—from data aggregation and automated analysis to knowledge‑base driven requirement enhancement and fully automated test case generation—ultimately turning quality assurance into a collaborative human‑AI partnership.
This article reviews six open‑source MCP servers—Bright Data, Graphiti, GitIngest, Terminal, Code Executor, and MindsDB—showing how each extends large language models with web scraping, long‑term memory, code navigation, command‑line control, sandboxed Python execution, and multi‑source data integration, complete with practical code examples.
This article explores DeepSeek's open‑source large‑model breakthroughs, PingCAP's AI‑enhanced database roadmap, TiDB.AI's retrieval‑augmented generation framework, the unified TiDB data engine, and practical Q&A insights on knowledge‑graph construction, vector search, and AI‑driven SQL generation.
The article argues that to unlock the disruptive potential of large language models in software engineering, they should be integrated from the requirements stage through the whole software development lifecycle, providing full semantic context, boosting overall efficiency, and creating an evolving project knowledge graph.
The article explains how large AI models are evolving from simple chatbots into reasoning-powered agents integrated with massive domain knowledge, creating powerful “industrial brains” that can understand and solve complex real‑world problems, reshaping the future of IT and the internet industry.
This article explains how the rapid development of large language models and knowledge graph technologies creates new opportunities for enterprise knowledge management, outlines the challenges of massive unstructured data, describes the architecture and core data flow of a corporate knowledge brain, and showcases key technologies and real‑world applications.
The article explains how AutoDev’s Context Worker pre‑generates semantic code context to improve RAG performance, outlines the limitations of vector‑based retrieval, describes the tool’s multi‑language AST analysis, knowledge‑graph construction, and provides command‑line usage examples for integrating the generated context into AI‑driven development workflows.
The article examines Glean’s evolution from an enterprise‑search startup to a comprehensive Work AI Platform, detailing its market growth, competitive positioning, technical architecture—including data connectors, knowledge graphs, custom models, and agent reasoning—and the strategic challenges it must overcome to sustain its lead.
This article presents a comprehensive overview of the security challenges in HarmonyOS native ArkTS applications and describes the design and implementation of a specialized static analysis framework—including source extraction, data‑flow and inter‑function analysis, knowledge‑graph construction, and risk inference engine—while also outlining integration into development pipelines and future research directions.
This article explores how knowledge graphs can be tightly integrated with large language models through prompt engineering, fine‑tuning, retrieval‑augmented generation, reasoning collaboration, and knowledge agents, outlining technical pathways, practical implementations, and future research directions across AI domains.
This article demonstrates how to construct an AI‑powered medical knowledge base for diabetes treatment by preprocessing literature, performing semantic chunking, generating BioBERT embeddings, storing them in a FAISS vector database, and using a RAG framework together with a knowledge graph to retrieve and generate accurate answers.
This article details Alibaba's security team's comprehensive approach to detecting ArkTS code vulnerabilities in HarmonyOS apps, covering architectural design, static analysis techniques, knowledge‑graph modeling, CI/CD integration, and future AI‑driven enhancements.
Researchers from Ant Group and Zhejiang University propose K-ON, a multi-token parallel prediction method that enables large language models to perceive knowledge graph entities through entity-level contrastive learning, achieving superior performance, lower cost, and higher efficiency on KG completion benchmarks.
The article explains Microsoft’s open‑source PIKE‑RAG system, detailing its four progressive stages—from knowledge‑base construction to creative multi‑agent reasoning—while describing the underlying modules, chunking strategies, multi‑granularity retrieval, and code snippets that enable specialized domain understanding and inference.
This issue reviews China's first open‑source short‑film model SkyReels‑V1, DeepSeek's Native Sparse Attention breakthrough, xAI's massive Grok‑3 deployment on 200k H100 GPUs, and a knowledge‑graph‑guided RAG framework, highlighting their performance gains, architectural innovations, and industry impact.
This article explains how NebulaGraph’s large‑scale graph database can be used to construct real‑time risk‑control knowledge graphs, describes practical applications such as community detection and path analysis, and explores how large language models enhance graph queries through Text‑to‑GQL, agents, exploration chains, and semi‑structured knowledge extraction.
Faced with billions of legal documents and the shortcomings of keyword search, Chinese legal professionals are turning to the AI‑powered YuanDian WenDa engine, which leverages Baidu's Wenxin model, a structured legal database, and prompt‑engineering to deliver trustworthy, citation‑rich answers and rapid research reports.
This article examines how programming large models combined with code knowledge graphs can overcome the limited context of traditional code‑completion tools, detailing key techniques, trigger strategies, context acquisition methods, model fine‑tuning practices, current challenges, and future research directions for intelligent, repository‑wide code suggestions.
At the 2024 Global Machine Learning Conference in Beijing, 360 AI Research Institute showcased cutting‑edge multimodal large‑model research, fine‑grained open‑world object detection, and document understanding technologies, highlighting open‑source releases, real‑world deployments, and competitive achievements in AI competitions.
This article explains how to build a GraphRAG system that integrates knowledge graphs and vector embeddings using PolarDB, Alibaba Cloud's Tongyi Qianwen LLM, and LangChain, demonstrating improved retrieval‑augmented generation accuracy through combined graph‑and‑vector search.
The article details Ant Group’s AI‑driven security parallel plane and intelligent threat detection system, its DKCF‑based architecture, key modules for data correlation, unknown threat discovery, alarm reduction, and knowledge‑graph integration, and its recognition in the 2024 AI Pioneer Case Collection.
This article reviews the China Academy of Information and Communications Technology's advancements in large‑model‑driven data intelligence, covering development trends, key deployment technologies such as prompt engineering, fine‑tuning and RAG, emerging application paradigms, challenges, and a series of newly drafted standards to guide industry adoption.
This article analyzes the Meta Comprehensive RAG (CRAG) benchmark, detailing its three tasks, evaluation metrics, and the champion DB3 team's end‑to‑end solution that combines data preprocessing, dual‑stage retrieval, prompt engineering, LoRA‑based fine‑tuning, and public data augmentation to achieve top scores across all tasks.
The article examines how large language models are reshaping JD Retail's e‑commerce search and recommendation pipelines, detailing industry evolution, technical challenges such as knowledge hallucination, intent understanding, personalization, cost, and safety, and presenting JD's end‑to‑end AIGC architecture, data preprocessing, alignment, evaluation, and next‑generation AI search solutions.
This article shares the exploration and practice of using Baidu's Wenxin large model to build a commercial intelligence assistant, highlighting its impact on business revenue and user experience, code generation, knowledge graph integration, database query optimization, and visual analytics for enhanced data analysis.
The presentation by Dr. Su Yu outlines challenges such as data sparsity and delayed learning effects in AI‑driven education, introduces three technical breakthroughs—domain‑specific LLM training, small‑knowledge learning via hierarchical knowledge graphs, and reinforcement‑based cognitive recommendation—and showcases product applications like the Frog Programming Platform, AI Programming Learning Machine, and digital‑human AI recorded courses.
This article describes how a commercial search platform iteratively upgrades its system by structuring business knowledge into a knowledge graph, applying multi‑stage entity extraction (CRF, Electra‑CRF, GLM‑3, OCR), and leveraging large language models to improve relevance, user experience, and revenue.
The KAG framework combines knowledge‑graph‑driven symbolic reasoning with large language model generation to improve accuracy, reduce hallucinations, and enable controllable, domain‑specific AI services such as government and medical Q&A, with open‑source support via OpenSPG and TuGraph‑DB.
The 2024 Inclusion·Bund Conference hosted a forum on "Large Model Creativity Boundaries and Application Imagination," featuring leading AI experts who discussed agents, multimodal technology, knowledge graphs, announced a new industry alliance, unveiled three major model products, and presented a trustworthy AI framework report for finance, healthcare, and government sectors.
This article presents a comprehensive overview of 360's document cloud knowledge management and Q&A scenarios, discussing business pain points, large‑model challenges, the advantages of the intelligent document solution, and how knowledge graphs enhance retrieval‑augmented generation and document standardization for AI‑driven enterprise applications.
This article presents a comprehensive exploration of knowledge‑graph‑based modeling, neural‑symbolic multi‑hop reasoning, and large‑model‑driven question answering applied to precise medication decision‑making in hypertension, detailing system architecture, experimental evaluations, real‑world deployments, and future research directions.
On June 25, 2024, OpenKG hosted a hybrid academic salon at Alibaba Cloud Valley, featuring expert talks on knowledge graphs, large language models, and their joint impact on AI, with presentations from leading researchers and industry professionals across multiple sessions.
The article details Ant Group’s security parallel aspect fusion AI solution, selected as an exemplary case at the 2024 World Intelligent Industry Expo, explaining its multi‑dimensional data collection, large‑model integration, baseline construction, knowledge‑graph generation, and superior threat‑detection performance.
This article surveys the rapid expansion of large AI models, covering prompt engineering, structured prompts, retrieval‑augmented generation, AI agents, fine‑tuning strategies, vector database technology, knowledge graphs, function calling, and their collective role in moving toward artificial general intelligence.
This article presents a comprehensive overview of conditional and multimodal knowledge graphs, covering their background, construction pipelines, extraction techniques, dataset creation, semi‑supervised learning strategies, and how they can be fused with large language models for enhanced reasoning and application in tasks such as intelligent QA and video scene graph generation.
This article presents Shopee's comprehensive approach to constructing an e‑commerce knowledge graph, detailing the challenges of heterogeneous data, multi‑language handling, entity disambiguation, and the integration of deep learning and large language models to improve product matching, recommendation, and operational efficiency.
This article presents Fudan University's Knowledge Factory Lab research on domain large models, covering background, challenges, data selection, source‑enhanced tagging, capability improvements, self‑correction, collaborative workflows, and retrieval‑augmented generation for practical AI deployment.
This article presents Shopee's comprehensive exploration of building an e‑commerce knowledge graph, detailing its challenges, construction pipeline, AI‑driven extraction and fusion techniques, multilingual and multimodal modeling, and practical applications ranging from search and recommendation to AI assistants and real‑time updates.
This article explores the concept of intelligent risk control in banking, detailing its AI‑driven architecture, current challenges such as external data costs and model‑deployment friction, and proposes an integrated operational framework that leverages big data, knowledge graphs, and MLOps to enhance risk detection and decision‑making.
The article explores the concept, challenges, and future directions of intelligent banking risk control, emphasizing data integration, AI-driven modeling, feature engineering, MLOps, knowledge graphs, and large‑model applications to create a unified, automated risk management platform.
This article explains why knowledge graphs are crucial for industrial applications, describes the main product forms and architectural considerations, and shares real‑world case studies illustrating how AI, large models, and graph databases can be combined to improve knowledge management and decision‑making.
This article explains intelligent risk control as a synergy of expert experience and algorithmic decision‑making, outlines its definition, expert human systems, digital algorithmic systems, and explores advanced AI methods such as reinforcement learning, large language models with knowledge graphs, adversarial learning, graph neural networks, and a practical supply‑chain case study.
This article examines why large language models often produce hallucinations in industry question‑answering, defines the phenomenon, explores its data and training origins, proposes evaluation metrics, and presents practical strategies—including high‑quality fine‑tuning data, honest refusal mechanisms, advanced decoding methods, and external knowledge‑graph augmentation—to reduce hallucinations and improve reliability.
This article presents a comprehensive overview of how large AI models are being applied in finance, covering development trends, practical use cases, knowledge‑graph integration, safety mechanisms, ethical considerations, and the evolving ecosystem of model‑centric solutions.
GraphAr is an open, standardized file format for storing large-scale graph and knowledge‑graph data, designed to address the challenges of graph computing ecosystems, offering superior performance and interoperability compared to existing formats, with growing community support and promising applications in data lakes and analytics.
This article examines how large language models enhance human‑machine interaction and can be combined with knowledge graphs to improve factual Q&A, task‑oriented services, and structured decision‑making, while highlighting ongoing challenges and the enduring role of knowledge graphs in structured domains.
The article examines the high construction cost and lack of unified standards in knowledge graphs, explains why large language models cannot fully solve core issues such as hallucination and multi‑hop reasoning, and argues that a new, unified semantic framework integrating large models is essential for future progress.
Large language models enhance human‑machine interaction and natural language understanding, but knowledge graphs remain essential for structured, low‑cost decision making, factual retrieval, and domains like finance; combining both can improve conversational systems, while ongoing challenges in knowledge graph construction persist, as highlighted for the upcoming DataFunSummit2024.
This article details the design, integration, and operational practices of a comprehensive user and item profiling tag system, covering tag taxonomy, construction methods, update cycles, access strategies, algorithmic implementations, and real‑world applications such as marketing, attribution analysis, and A/B testing.
The article details how JD.com leverages domain‑specific and generic knowledge graphs to enhance multimodal product information, improve controlled text generation, and boost LLM performance for e‑commerce copywriting, covering model architecture, copy‑only mechanisms, token‑type encoding, experimental results, and practical deployment scenarios.
This article presents a comprehensive overview of how knowledge graphs are integrated into e‑commerce AIGC pipelines, covering domain‑specific and generic KG‑driven text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language models in commercial settings.
This article examines the challenges of using large language models for industry Q&A, defines hallucination phenomena, evaluates their causes and impact, and proposes a set of strategies—including high‑quality fine‑tuning data, honest alignment, advanced decoding, and external knowledge‑graph augmentation—to reduce hallucinations and improve answer reliability.
Ant Group shares its comprehensive knowledge graph initiatives, detailing the fundamentals, construction pipeline, fusion techniques, cognitive representations, diverse business applications, and the emerging synergy between knowledge graphs and large language models, illustrating how graph-based AI enhances accuracy, interpretability, and downstream services.
This article presents a comprehensive overview of how knowledge graphs are leveraged in e‑commerce AIGC pipelines, detailing domain‑specific and general graph‑based text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language model integration.
This article reviews recent research on augmenting language and multimodal models with external knowledge sources and tool‑calling mechanisms, covering three systems—OREO‑LM for knowledge‑graph reasoning, REVEAL for multi‑source visual‑language pretraining, and AVIS for dynamic tool selection—and their experimental results and implications.
This article examines how large language model technology can be leveraged for intelligent operation of wireless communication networks, analyzing its advantages, current challenges, platform architecture, experimental validation, and future research directions within the telecom industry.
This article describes how China Telecom's AI R&D Center designs and implements a network operations event knowledge graph using AI techniques, graph databases, and UIE models to improve fault handling, automate recommendations, and enhance intelligent assistance for telecom network maintenance.
This article presents Ant Group's work on knowledge graphs, covering the fundamentals, construction pipeline, fusion techniques, cognitive modeling, real‑world applications, and the emerging synergy between knowledge graphs and large language models, while highlighting technical challenges and future directions.
This article presents a comprehensive overview of data engineering practices, pre‑training data composition, automated model evaluation techniques, and the synergistic use of knowledge graphs within large‑scale AI model research, highlighting pipelines, quality criteria, and practical case studies.
The 17th China Conference on Knowledge Graph and Semantic Computing (CCKS 2023) will be held in Shenyang from August 24‑27, featuring the theme “Knowledge Graph Empowering General AI”, a full program of workshops, invited talks, special forums, industrial sessions, registration details, and links to the OpenKG initiative.
The article explains data fabric architecture as a promising approach for enabling data exchange across distributed systems, outlines its three design patterns, describes key technical components such as data virtualization, data catalog, and knowledge graphs, and discusses the trade‑offs, costs, and limitations that organizations must consider.
This article introduces the fundamentals of knowledge graphs, explains how graph neural networks can be adapted for knowledge graph reasoning, presents specialized GNN designs such as CompGCN and RED‑GNN, and discusses experimental results, interpretability, efficiency improvements, and future research directions.
This article presents Taiping Jinke's end‑to‑end insurance anti‑fraud risk control framework, detailing industry pain points, core AI‑driven capabilities, platform blueprint, specific car and health insurance fraud engines, and real‑world case studies that illustrate how big‑data, machine‑learning and knowledge‑graph techniques are integrated into business processes.
This article explains the concept of Data Fabric (data weaving), its background, definition, relationship with data middle platforms, data governance and DataOps, compares it with related concepts, and outlines future development directions and challenges.
The ACL 2023 paper introduces FashionKLIP, an e‑commerce visual‑language model enhanced by a multimodal concept knowledge graph, detailing its automated knowledge graph construction, dual‑stream training strategy, and superior performance on FashionGen retrieval benchmarks compared to state‑of‑the‑art methods.
Three papers from Alibaba Cloud's PAI platform were selected for ACL 2023, showcasing FashionKLIP for e‑commerce image‑text retrieval, ConaCLIP's lightweight dual‑encoder distillation, and a fast domain‑specific diffusion model, all of which will be open‑sourced for the AI community.
This article presents Ant Group's large‑scale financial knowledge graph platform, detailing its semantic knowledge representation, hybrid graph model, distributed management architecture, core capabilities such as knowledge evolution and cross‑domain fusion, and showcases applications like anti‑fraud capital‑flow analysis and future DataFabric‑oriented knowledge sharing.
In a Shanghai launch event for Altair RapidMiner, Prof. Xiao Yanghua explained why data is a strategic resource, outlined the challenges of data value realization, introduced the concept of data intelligence driven by both data‑driven and knowledge‑driven systems, and discussed emerging trends such as low‑code platforms, large‑model integration, and the shift of AI applications from consumer to industrial domains.
This article presents OPPO Research Institute's recent advances in large‑model AI, detailing the CHAOS pretrained model that topped the CLUE leaderboard, the knowledge‑enhanced training pipeline, and the GammaE model for multi‑hop reasoning over knowledge graphs, together with experimental results and practical training tips.
This presentation details Shopee's approach to constructing a multilingual product knowledge graph, covering ontology modeling, data acquisition, fusion techniques, and practical applications, while discussing challenges, model architectures, and future directions for large‑scale e‑commerce AI systems.
This article presents a comprehensive overview of applying causal inference to Alibaba’s Business Intelligence platform, detailing fundamental concepts, the merchant intelligent diagnosis system architecture, key technologies such as hybrid causal network discovery (HCM) and deep attribution, and showcases the resulting operational impact.
This article describes how a power‑industry knowledge graph is built using AI, big‑data and cloud techniques, outlines its multi‑dimensional structure, and demonstrates various application scenarios such as personal achievement aggregation, professional learning, job training, generic knowledge services, and decision support for power production.
This guide presents a comprehensive methodology for building knowledge graph schemas that decouple structural representation from semantic meaning, covering schema design, attribute semantic standardization, concept modeling, multi‑relational and hypergraph techniques, and practical steps for implementation across complex business domains.
This article presents the development and deployment of the TransE+ knowledge‑graph embedding model on the Plato graph‑computing platform, detailing its architectural upgrades, training optimizations, performance gains, and business‑oriented adaptations for large‑scale real‑world applications.
This article explains how knowledge‑graph technology combined with artificial‑intelligence methods can enhance intelligent manufacturing by improving quality and reliability through advanced root‑cause analysis, detailing development trends, analytical techniques, challenges, practical frameworks, and real‑world case studies.
This article reviews the development of event extraction, explains its importance for knowledge graphs, surveys four major algorithmic paradigms, introduces the OmniEvent open‑source toolkit with its unified benchmark and modular design, and outlines future research directions such as document‑level extraction and event relation modeling.
The presentation introduces a multimodal deep neural network (MDNN) that integrates drug knowledge graphs and heterogeneous drug features to predict drug‑drug interactions, demonstrates state‑of‑the‑art performance on a large IJCAI‑2021 dataset, and discusses its architecture, evaluation, and future challenges.
The article examines ChatGPT's rapid adoption, the rise of generative AI, and how integrating external knowledge sources such as knowledge graphs can improve factual accuracy, enhance downstream tasks, and enable a variety of ChatGPT+ applications ranging from intelligent search to office automation tools.
This article describes how the Industrial and Commercial Bank of China's software development center created the AI‑driven digital employee “Ruan Xiaoyan,” detailing its functions, user touchpoints, and practical applications such as intelligent customer service, smart workflow automation, and proactive reminders that enhance IT operations efficiency.
This article details Meituan's hotel‑travel knowledge graph, describing its background, scene‑recognition challenges, multi‑layer graph architecture, technical modules for knowledge mining, relation discrimination and supply tagging, and its practical applications in search, recommendation, ranking, and risk control, while also outlining future directions and a Q&A session.
The forum explores the rapid growth and complexity of knowledge graphs, addressing storage and computation challenges through expert talks on graph database storage, query languages, practical implementation, and large‑scale financial knowledge graph platforms, offering attendees deep technical insights and hands‑on guidance.
On March 18, 2023, Dr. Zhang Yongqi will deliver an online talk at DataFun Summit 2023 about knowledge‑graph reasoning using graph neural networks, covering recent advances, applications, challenges, and offering free registration for participants interested in AI and knowledge‑graph technologies.
This guide introduces the challenges in healthcare, explains how artificial intelligence is already reshaping the field, and provides a curated list of recent articles on smart medicine for readers to explore the emerging AI‑healthcare integration.
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.
The article explains how combining human expertise with AI techniques—through problem decomposition, model selection, feature engineering, and knowledge‑graph construction—enables practical NLP solutions for document extraction and intelligent Q&A, illustrating the process with contract‑field extraction case studies.
This article reviews explainable knowledge graph reasoning, covering its background, core concepts, downstream applications, major reasoning methods, motivations for interpretability, recent advances such as hierarchical and Bayesian reinforcement learning, meta‑path mining, and future research directions.
This presentation details how Meituan leverages knowledge‑graph technology to model hotel and travel business characteristics, perform scene cognition, build a multi‑layer knowledge graph, and design a five‑stage search architecture that combines precise and generic queries with AI‑driven ranking and explainable recommendation techniques.
This article presents a comprehensive overview of intelligent entity recommendation for search, covering scenario introduction, relevance modeling, handling sparse query and entity data with graph‑based methods, and multi‑domain, multi‑scenario ranking techniques to improve user experience.
This talk presents a personalized news recommendation system that leverages knowledge graphs to enhance recommendation accuracy, explainability, and user interest modeling, detailing background, graph construction methods, multi‑task deep learning architecture, experimental results, and future research directions.
