1 views collected around this technical thread.
A comprehensive glossary for e‑commerce product managers that defines key metrics such as GMV, SKU, UV, CVR, AOV, CPC, and models like RFM and AARRR, while highlighting common pitfalls and practical strategies for data‑driven decision making.
The article explains how Douyin's recommendation system uses machine‑learning and deep‑learning models to predict user actions, assign value weights, and dynamically adjust scores, highlighting both its efficiency in large‑scale content distribution and its inherent limitations compared to human understanding.
The fourth round of supermarket checkout simulation reveals hidden complexities such as receipt‑paper replacement, cash‑change shortages, and cart blockages, leading to a set of core performance‑testing design principles that emphasize realistic user behavior modeling, data volume, environment fidelity, diversity, and iterative feedback.
This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.
The article explains retention analysis models, their importance for user loyalty, outlines offline Hive architecture, then shows how ClickHouse’s retention() function and columnar storage dramatically speed up multi‑day retention calculations, providing SQL examples and practical guidance for product analytics.
This study analyzes JD e‑commerce operational data from February to April 2018, employing Python and SQL to compute key metrics such as PV, UV, conversion rates, attrition, purchase frequency, time‑based behavior, funnel analysis, retention, product sales, and RFM segmentation, and provides actionable recommendations for improving user engagement and sales performance.
This article examines various user behavior recording methods—including WebRTC video capture, canvas-based screenshot recording, and DOM snapshot recording with rrweb—detailing their technical implementations, advantages, limitations, and suitable application scenarios for product analysis, debugging, and automated testing.
This article explains ten data‑analysis techniques for enhancing user experience—such as behavior tracking, A/B testing, sentiment analysis, and personalization—and provides concrete SQL code snippets that illustrate how to import, query, filter, sort, aggregate, join, update, delete, and back up data in relational databases.
This article explains the concept of data tracking (埋点), outlines its key application scenarios such as exposure, click, and page‑event tracking, describes the end‑to‑end workflow from requirement gathering to deployment and post‑analysis, and summarizes the practical “seven‑word” checklist for successful implementation.
The article examines repurchase strategies for game item recommendations, analyzing various recommendation scenarios, their specific challenges, item classification based on purchase density and repurchase rates, and practical guidelines for applying the strategy across permanent shop, limited‑time gift packs, and refreshable recommendations.
This article presents a comprehensive Python-based analysis of a large game dataset (2.29 million records, 109 fields), covering user registration trends, payment rates, ARPU/ARPPU calculations, level‑based spending behavior, and consumption patterns of resources and acceleration items, with visualizations and actionable conclusions.
This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.
This article explains why front‑end tests that concentrate on implementation details become fragile and time‑consuming, and argues for writing tests that mimic real user interactions using Testing Library, while recognizing that many small functions lack independent business value and should be tested at the UI level instead of as isolated unit tests.
This article explains the concept of event tracking (埋点), its importance for capturing user behavior, outlines the four‑module architecture of a tracking system, compares code‑based, visual and full tracking methods, describes data models, storage, management, and presents a practical case study with analysis techniques.
The article describes Alibaba Moments’ advertising creative optimization platform, which uses hybrid bandit models that combine visual‑aware ranking priors with exploration‑exploitation algorithms such as Thompson Sampling and LinUCB to dynamically select whole creatives or individual elements, improving click‑through rates and mitigating cold‑start challenges.
DoKit’s one‑machine‑multi‑control lets an Android host device manage slave devices over a LAN without extra permissions or code intrusion, dramatically streamlining functional regression and compatibility testing while supporting user‑behavior recording, and is slated for open‑source release with future extensions to Flutter and Web.
This article surveys recent research on extracting and expanding hot topics from short texts by constructing user‑behavior graphs, applying graph‑embedding techniques, and leveraging multi‑task learning to improve recommendation relevance, timeliness, and cold‑start handling in large‑scale platforms.
This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.
This article describes how the frontend team designed and implemented a comprehensive user behavior data collection and analysis platform, covering its business value, overall architecture, SDK-based data gathering, event interception, processing pipelines, analytics dashboards, and practical insights for product and operations teams.
This article explains how to choose and combine appropriate evaluation metrics for recommendation systems by considering the specific scenario, business model, offline versus online testing, ecosystem balance, and user behavior, providing practical methods and a concise summary of common metric types.