JD.com's Unbounded Marketing Platform won the Big Data Product Award at the 2018 Big Data Industry Summit, showcasing its innovative data-driven marketing solutions for brands and consumers.
The article explains the core concepts of recommendation engines—user profiling and data classification—describes how large‑scale data processing tools are used to build models, and outlines common offline and A/B testing approaches for evaluating recommendation performance.
This article presents a comprehensive overview of Toutiao's recommendation system, detailing its three‑dimensional modeling approach, real‑time training pipeline, feature engineering, content and user analysis techniques, evaluation methodology, and the extensive content‑safety mechanisms employed to ensure reliable and responsible information distribution.
This article provides a comprehensive overview of Toutiao's recommendation system, covering its three‑dimensional modeling approach, feature engineering, real‑time training pipeline, recall strategies, user‑tag generation, evaluation methodology, and content‑safety mechanisms.
Meituan‑Dianping’s DSP coarse‑ranking filters large ad candidate sets by scoring ads with user‑profile, weather, and keyword scenario models—using frequent‑itemset mining, AdaBoost, and TF/IDF—then aggregates these scores via a linear‑regression model to select high‑relevance ads for fine‑ranking, boosting click‑through and conversion rates.
This article explains what user profiling is, why it’s crucial for recommendation systems, outlines key dimensions such as personal attributes, status, and interests, describes algorithms like classification and autoregressive models, and details offline and real‑time computation methods, evaluation techniques, and practical examples.
This article explains what recommendation systems are, their purpose, differences from search, typical multi‑layer architecture, real‑time and offline processing flows, user feedback handling, and key metrics for evaluating their effectiveness.
This article examines the rise of organized “wool‑pulling” fraud groups during China’s Double‑11 shopping festival, outlines their tools and tactics, and details NetEase Cloud Security’s multi‑layered anti‑fraud system—including captcha, SMS verification, IP rules, device fingerprinting, rule engines, user profiling, network graph analysis, and blacklist strategies—to protect e‑commerce platforms.
This article explores how simple tagging and user profiling underpin modern recommendation systems, contrasting tag‑based, flexible representations with traditional hierarchical classifications, and examines practical applications such as personalized advertising, industry research, and product optimization.
This article explains why Ctrip creates user profiles, describes the product and technical architectures, and details the data collection, computation, storage, high‑availability querying, and monitoring components that power its personalized travel recommendations and services.
This article presents a comprehensive overview of personalized recommendation systems, covering their application scenarios and value, user profiling, core algorithms such as content‑based and collaborative filtering, system architecture, performance and effect optimization techniques, and practical Q&A insights.
This article describes how Baidu Waimai’s user‑profile offline data system was equipped with a highly automated, coroutine‑based testing framework that dramatically improves field‑value accuracy verification, test coverage, and execution efficiency across strategy, ES, and API layers.
Meituan Delivery’s rapid expansion across multiple categories relies on detailed user profiling and machine‑learning models—such as high‑potential customer prediction, churn risk regression and Cox survival analysis—to personalize acquisition, retention, and scenario‑based cross‑selling, while addressing sparse behavior, unstructured data, and geographic context challenges.
Tencent's Glacier multi‑dimensional analysis platform combines massive user‑portrait tags with routine analytical reports, delivering fast, accurate real‑time queries across countless dimensional combinations, enabling analysts and operators to perform targeted operations and insights as product data continuously evolves.
At Meitu’s third Internet Technology Salon in Xiamen on November 26 2016, over 150 senior engineers heard Meitu’s Lu Rongbin detail the company’s progression from simple rsync scripts to a scalable mobile data and open statistical platform, while Tencent’s Zhao Shiyuan showcased the Glacier multi‑dimensional analysis system for fast, tag‑driven queries, underscoring collaborative technical exchange in South China.
This article explains the concept of user profiling, outlines its four-stage construction process, discusses the significance of tagging users, and details practical data modeling techniques—including static and dynamic data sources, weight calculations, and real‑world examples—aimed at improving precision marketing and recommendation systems.
The QQ Music recommendation system tackles cold‑start by first mixing Chinese and English tracks, then builds a six‑dimensional user profile (content, social, scenario, crowd, time, blacklist) and tags songs with six attributes, using content‑based, collaborative, matrix‑factorization and neural‑network models plus implicit co‑listening links, while acknowledging that final wisdom still comes from human listeners.
This article explains how comprehensive user profiling—combining static demographics and dynamic behavior logs—feeds recommendation engines, detailing data sources, feature extraction, ranking formulas, and the long‑term goals of delivering personalized, high‑quality content to users.
This article details 58 Daojia's data‑driven approach to building user‑portrait tags, covering tag construction, evaluation, and practical applications such as personalized recommendations, anti‑fraud measures, coupon distribution, and dynamic pricing, while outlining the underlying big‑data architecture and technical challenges.
The article explains the core concepts of big data, emphasizing data breadth across product lines, illustrates how comprehensive user profiling can drive personalized marketing and product improvements, and provides practical examples of cross‑product data analysis in e‑commerce, finance, travel, and gaming contexts.
This article details Meituan Dianping's data‑driven approach to precise marketing, describing the O2O marketing framework, a layered pyramid data system, profiling techniques, budget monitoring, and two real‑world case studies that together illustrate how big‑data technologies boost marketing efficiency on mobile platforms.
Meituan‑Dianping’s data‑driven precise‑marketing platform combines a layered pyramid architecture—data warehouse, service, and front‑end layers—with real‑time profile services powered by Redis and Elasticsearch, offering tools such as Hoek, Cord, and Cloud/Star to automate audience selection, coupon recommendation, and KPI monitoring, illustrated by food‑delivery user discovery and WeChat red‑packet coupon case studies, and guided by principles of reusable models and SOA decoupling.
This article walks through designing and implementing a full‑stack recommendation system for 58转转, covering product frameworks, user and item profiling, RFM modeling, personalized tagging, classification‑based and collaborative‑filtering approaches, and practical deployment tips.
This article explains the significance of personalized recommendation, distinguishes it from traditional push services, outlines typical application scenarios, and details a step‑by‑step approach—including user profiling, behavior sampling, algorithm modeling, machine learning, and content lifecycle management—to build effective recommender systems.
The article explains how search engine techniques such as large‑scale candidate recall, fine‑grained ranking, user profiling, and multi‑objective optimization are applied to news personalization at Toutiao, highlighting data sampling, machine‑learning pipelines, challenges of news freshness, and architectural evolution.
This article explains how Tencent uses big‑data collection, user profiling, and AI‑driven risk learning engines to detect and block malicious accounts, proxy IPs, and fraudulent activities across e‑commerce and other platforms, detailing the architecture, algorithms, and practical defenses employed.
This article presents expert Q&A on effective user behavior analysis techniques for building detailed user profiles and compares mainstream stream‑processing solutions, outlining key factors such as latency, throughput, parallelism, and fault tolerance for selecting the right real‑time data platform.
Mobile search is evolving beyond traditional keyword queries, with emerging trends in precise user profiling, crowdsourced data, voice and natural language understanding, deep linking, machine learning, and structured, intelligent result aggregation, promising a more personalized, context‑aware, and seamless search experience on smartphones.
