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 recounts a year-long journey to become a data mining engineer, explaining the role’s value, required skills, tools such as Excel, Tableau, SQL, Python, Scala, Spark, and machine‑learning techniques, and offers practical steps for aspiring professionals.
Python is favored for data‑mining algorithms because its clear syntax, built‑in advanced data structures, easy text handling, extensive libraries, and widespread community support outweigh its slower execution speed compared to Java or C, allowing rapid development and seamless integration with high‑performance code when needed.
By mining over a million computer‑related job postings with weak‑supervised learning and a BiLSTM NER model, this article quantifies how programming languages, development tools, and hardware skills translate into salary value, offering data‑driven guidance for developers and new graduates.
The first Alibaba data‑mining forum in Hangzhou gathered top academics and industry leaders who discussed large‑scale online precise matching, the role of distributed versus single‑machine learning, and the benefits and limitations of deep learning in modern AI applications.
In a Q&A session, a Tencent social ads data mining expert addressed competition participants' questions on data delays, full‑set versus sliding‑window statistics, dataset authenticity, Bayesian smoothing, feature selection, handling missing values, large‑scale training, feature interactions, model stacking, online mini‑batch training, and provided reference resources.
Alibaba and Ant Group had five research papers accepted at the 2017 KDD conference, covering topics such as intelligent ad pricing, graph‑based behavior prediction, conv‑RNN semantic encoding, cascade ranking, and a distributed learning platform, all of which have been applied in their production systems.
This article provides a comprehensive overview of customer segmentation, detailing its definition, multi‑dimensional challenges, seven implementation guidelines, five systematic steps, ten frequent pitfalls, and a practical RFM model case study using big‑data mining techniques.
The article compiles ten participants' reflections on the Tencent Social Ads university algorithm contest, detailing their data analysis, feature engineering, model selection, challenges faced, and practical advice, while encouraging readers to vote for the most helpful contributions.
The article explains that the ultimate value of big data lies in optimizing resource allocation by first crowdsourcing massive data, then fully mining it to uncover truth, and finally using those insights across industries such as transportation, advertising, finance, and more.
The article examines how extensive data collection and analysis of adult performers and their content reveal surprising demographic patterns and drive innovative business models, product development, and technology adaptations within the porn industry, illustrating the practical impact of big‑data insights beyond traditional sectors.
This article demonstrates a Python-based movie recommendation approach that crawls Douban user data, categorizes ratings, computes Pearson correlation to identify like‑minded users, and generates weighted movie suggestions, complete with code snippets for data handling, similarity calculation, and recommendation generation.
The fourth Meitu Internet Technology Salon showcased practical AI and machine learning uses, highlighting Meipai’s text‑anti‑spam, hot‑topic detection, sentiment analysis and personalized video search, while Baidu demonstrated ML‑driven business intelligence tools for multi‑source data mining, user profiling, and intelligent enterprise and HR management.
This article explains a six‑layer data analysis framework—from raw data sources and data warehouses through exploration, mining, and visualization—while also reviewing common analysis tools such as R, SAS, SPSS, and describing typical statistical techniques and presentation methods.
This article explores how software testers can leverage big data and data‑mining techniques, particularly a Naive Bayes classifier, to objectively prioritize defect fixes, improve testing processes, and address uncertainties inherent in traditional testing workflows.
This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.
This article reviews a comprehensive big‑data lecture covering traditional databases, Hadoop ecosystems, commercial big‑data platforms, computing models, analysis techniques, visualization, and leading vendors, highlighting how these technologies shape today’s data‑driven enterprises.
This article walks through the fundamentals of recommendation systems, explaining collaborative filtering and various similarity measures—including Manhattan, Euclidean, Minkowski, Pearson correlation, and cosine similarity—while discussing their suitability for dense, sparse, or biased rating data and introducing K‑Nearest Neighbors for practical implementation.
This article introduces a programmer‑focused guide to data mining, explaining how personalized recommendation systems work, illustrating historical and modern examples, and outlining the book’s practical Python‑based approach to mastering big‑scale data analysis.
Data mining, an interdisciplinary field of computer science, involves tasks such as anomaly detection, clustering, classification, and regression, follows standardized processes like KDD, CRISP-DM, and SEMMA, and often leverages machine learning techniques—including supervised, unsupervised, and reinforcement learning—to extract valuable insights from complex datasets.
This article explains what data is, classifies it into structured, semi‑structured and unstructured forms, describes data mining, databases, data warehouses, the full data lifecycle, and surveys the big‑data ecosystem including storage, batch and real‑time processing, resource scheduling, and visualization technologies.
Apache TinkerPop, now a top-level Apache project, offers a powerful graph computing framework with Gremlin, supporting real-time transactional processing and batch analytics across languages, scalable from single machines to massive clusters, making it essential for data mining, analysis, and large‑scale graph applications.
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.
This article outlines ten practical key points—including leveraging explicit and implicit feedback, hybridizing algorithms, handling temporal and geographic factors, exploiting social ties, solving cold‑start issues, optimizing presentation, defining clear metrics, ensuring real‑time updates, and scaling big‑data processing—to help engineers design effective intelligent recommendation systems.
As Meipai’s user base exploded, the platform shifted from manual curation to sophisticated personalized recommendation algorithms—leveraging machine‑learning and data‑mining techniques, iterating through multiple generations, overcoming scalability and relevance challenges, and delivering rapid solutions that inspire future recommendation system designs.
The article explains why small data are essential for avoiding common big‑data mining traps, illustrates pitfalls through real‑world examples, and offers practical methods—incremental improvement, analogical reasoning, and simple modeling—to harness weak signals for more reliable decision‑making.
This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor cell identification, wine quality assessment, fraud detection, recommendation systems, and more.
This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor identification, wine quality assessment, fraud detection, recommendation systems, and authorship analysis.
This article details how telecom operators can identify fraudulent ModemPOOL (cat‑pool) terminals and predict churn using data‑driven clustering and day‑interval warning models, covering metric selection, data exploration, k‑means clustering, model deployment, and performance evaluation.
This article presents nine fundamental laws of data mining—covering goals, knowledge, preparation, experimentation, patterns, insight, prediction, value, and change—explaining how business objectives and domain expertise drive each stage of the CRISP‑DM process and why technical metrics alone cannot guarantee success.
Suning’s search team automatically extracts domain‑specific synonym pairs from massive query‑click logs using candidate extraction, multi‑feature similarity calculations, and Apriori pattern mining, dramatically improving e‑commerce search recall and user experience.
This article introduces the concepts of classification and prediction in data mining, outlines their workflow, and provides concise explanations of six widely used classification techniques—decision trees, K‑Nearest Neighbour, Support Vector Machine, Vector Space Model, Bayesian methods, and neural networks—highlighting their principles, advantages, and limitations.
This article reviews the most frequently used distance and similarity formulas in machine learning and data mining, explaining their definitions, mathematical properties, practical examples, and when each metric is appropriate for measuring differences between data points or probability distributions.
At QCon2014, Baidu Research’s Shen Zhiyong showcased the company’s massive big‑data engine—20,000 PB storage and daily processing of up to 100 PB—highlighting open platforms like Baidu Brain and real‑world prediction projects for tourism, the World Cup, disease outbreaks, and UN collaborations, while urging industry‑wide data‑driven transformation.
