This article describes a Python‑implemented shopping mall project that provides separate merchant and customer desktop applications built with PyQt5, details the system’s features, file structure, server‑client communication, database setup, execution steps, and includes the complete source code for reference.
The 2022 Global AI Technology Innovation Competition – Algorithm Challenge, co‑hosted by JD Retail and academic partners, brought together 12 finalist teams from over 3,000 entrants to tackle e‑commerce‑focused AI problems such as multimodal image‑text matching and product‑title entity recognition, highlighting real‑world business impact and fostering talent exchange.
The article details JD International's technical preparation for the 618 sales event, covering operational planning, DDoS mitigation with Cloudflare, performance testing methodologies, cross‑team collaboration, and real‑time monitoring to ensure system stability and a seamless shopping experience across multiple regions.
The article defines T‑level interactions—high‑traffic, high‑revenue e‑commerce events—and outlines how using the Taro framework, a unified H5 and mini‑program development stack, combined with a custom component library, automated testing, and robust error‑handling strategies can dramatically improve development efficiency, consistency, and reliability across massive seasonal campaigns.
This talk presents JD's intelligent creative ecosystem for advertising, detailing the construction of a content understanding system, AI-driven content generation (including OCR, image tagging, video summarization, and copy generation), and multimodal creative selection and distribution, highlighting challenges, solutions, and business impact.
The paper proposes a multimodal pre‑training framework that jointly encodes query text and item titles with images via shared and single‑stream towers, using MLM, MPM, QIC, and matching tasks, and demonstrates substantial Recall@K gains on a billion‑item e‑commerce catalog by leveraging visual cues to bridge the semantic gap.
This article explains why concurrency issues like overselling occur in e‑commerce, introduces distributed lock concepts and mainstream implementations (MySQL, Redis, Zookeeper/etcd), and provides a three‑stage quality‑assurance framework with code‑review checklists, testing methods, and data‑reconciliation techniques.
The 2021 Taobao New‑Product team boosted e‑commerce recommendation by redesigning the coarse‑ranking stage with a dual‑tower DSSM, low‑cost feature‑crossing, NOVA attention and multi‑task distillation from a fine‑ranking teacher, delivering up to +30‰ GAUC gain and 3‑5 % online CTR and click improvements.
This presentation details how JD applied reinforcement learning—using RNN‑based user state modeling and a DDPG framework—to improve e‑commerce search ranking by optimizing long‑term cumulative value, showing significant offline and online gains in conversion and GMV.
The article traces Taobao’s shopping cart evolution from a basic product‑management tool to a real‑time, AI‑enhanced platform, detailing business goals for user experience and conversion, four staged upgrades—including bundling, discount filtering, and seamless checkout—plus technical solutions such as Bloom filters, asynchronous pipelines, and nextRpc, while outlining operational expansions and future AI‑driven improvements.
This article examines the role of an order system in traditional e‑commerce enterprises, outlines its core functional modules, describes its upstream and downstream relationships, explains workflow and state‑machine design, and discusses future architectural evolution and best‑practice considerations.
This article explains how JD's intelligent recommendation selling point system leverages NLP, BERT, Transformer and pointer‑generator models to automatically create short, personalized product highlights, describing the technical background, system architecture, model training pipeline, online/offline monitoring, and the resulting business benefits.
The article reviews the Global Information System Stability Summit and JD's technical architect Li Junliang's detailed case study on the engineering practices, observability, chaos engineering, and resource‑scheduling innovations that enable JD’s e‑commerce platform to handle sales‑peak traffic that spikes hundreds of times over normal load.
This article explains the fundamental concepts, design requirements, and modeling approaches for payment account systems in e‑commerce, covering the distinction between payment and login accounts, transaction needs, accounting structures, entity and account models, and the detailed transaction model.
This article explores common e‑commerce backend challenges such as duplicate orders, order snapshots, cart storage, inventory oversell, logistics updates, account balance consistency, read‑write splitting, hot‑cold data separation, and sharding, and provides concrete design patterns and SQL examples to solve them.
This article explains the fundamental concepts, design requirements, and modeling techniques for payment accounts in e‑commerce systems, covering the distinction between payment and login accounts, transaction flows, accounting structures, internal and external accounts, and detailed entity, account, and transaction models.
This article analyzes how a home‑cleaning platform increased Chinese New Year service orders by designing a multi‑service ordering flow, comparing four packaging options, selecting a package‑recommendation model, eliminating three key friction points, and validating the solution through A/B testing and metric improvements.
This tutorial presents a complete Python + Django e‑commerce mini‑project, outlining its main features, required environment, step‑by‑step installation commands, configuration changes, database setup, and how to run the application locally for self‑learners.
This article presents the evolution of Alibaba's display advertising auction mechanisms, introducing the AIDA decision‑allocation framework, the Deep GSP multi‑objective smart auction, and the end‑to‑end Neural Auction, while discussing their economic theory, engineering platformization, business impact, and future research directions.
This article explains the fundamental concepts, design requirements, and modeling techniques for payment accounts and transaction flows in e‑commerce systems, covering distinctions between payment and login accounts, internal vs. external accounts, and detailed entity, account, and transaction models.
This article explains the distinction between payment accounts and login credentials, outlines essential design requirements such as transaction, accounting, reconciliation, risk control, and credit needs, and details entity, account, and transaction models for building comprehensive e‑commerce payment systems.
This article explains how to design a payment account system for e‑commerce, covering the distinction between payment and login accounts, design requirements, transaction and accounting models, internal and external accounts, and detailed account and transaction modeling to support security, risk control, and reconciliation.
The paper tackles Taobao Live’s cold‑start problem for new users by introducing a multi‑channel lifelong product‑sequence network that enriches purchase histories with side information, extracts relevance‑focused subsequences across five channels, and integrates them via target‑attention DIN, achieving substantial offline and online performance gains, especially for low‑activity users.
This article examines the role of order systems in traditional e‑commerce enterprises, outlines their core functional modules and design principles, illustrates system relationships with upstream and downstream services, and discusses future development trends, including modular architectures and multi‑system integration challenges.
The Baidu Guarantee platform provides a three‑layer architecture—data, basic services, and core systems—to enable merchants across Baidu’s ecosystem to enroll, disclose credit, access click records, and process compensation claims, delivering millisecond‑scale query performance and improving conversion through visible guarantee badges.
This article analyzes 2 million Taobao app user‑behavior records using AARRR funnel metrics and RFM segmentation, revealing daily and hourly usage patterns, conversion bottlenecks, product‑search mismatches, and offering data‑driven marketing recommendations to boost retention and sales.
The article presents a modular, extensible content‑delivery pipeline for e‑commerce—comprising datasource, transfer, filter, sorter, completer, validator, factory and iterator nodes—implemented in Java, with JSON‑driven filter schemas, to simplify complex, low‑latency content handling, improve flexibility, and ease operations across evolving business requirements.
This comprehensive guide walks you through evolving a monolithic e‑commerce application into a highly available, scalable, low‑latency, event‑driven microservices system capable of handling millions of requests, covering design patterns, principles, best practices, and technology choices.
The article presents JD's e‑commerce recommendation system, detailing its four‑stage ranking pipeline, multi‑objective optimization with personalized fusion, transformer‑based user behavior sequence modeling, fine‑grained behavior modeling, and multimodal feature integration, and shares experimental results and engineering optimizations.
This article explains the roles, architecture, core modules, workflow engine, inventory management strategies, and state machine design of order systems in traditional e‑commerce enterprises, offering insights into their future development and integration with other business services.
The article details how an e‑commerce product middle platform’s front‑end employs visualization technology, a uni‑render approach that shares a Vuex store between an iframe and its parent via a sandboxed Vue instance, and a qiankun‑based micro‑frontend architecture, while addressing cross‑domain and editor integration challenges.
This article provides a comprehensive technical overview of large‑scale distributed website architecture, covering characteristics, design goals, architectural patterns, performance, high availability, scalability, extensibility, security, agility, evolution stages, capacity estimation, and practical optimization techniques for e‑commerce platforms.
This article outlines the architectural principles, middle‑platform strategy, service layers, common components, cloud deployment, security measures, and detailed design diagrams for building a scalable, high‑concurrency e‑commerce transaction system using DDD, microservices, and cloud‑native technologies.
This article outlines a comprehensive e‑commerce system architecture that combines a large middle platform with a small front‑end, leveraging domain‑driven design, service‑oriented layers, cloud‑native deployment, and robust monitoring to achieve high scalability, flexibility, and reliability for complex transaction scenarios.
This article presents a comprehensive overview of Taobao's user‑generated content (UGC) sentiment analysis pipeline, covering background, task definition, challenges, model architecture—including RoBERTa‑based extraction, sentiment‑knowledge pre‑training, and graph augmentation—personalized impression ranking, business impact cases, and future research directions.
NextRPC introduces a multi‑segment payload RPC model that streams responses through successive "Next" stages, enabling parallel processing and decoupling of core and non‑core logic, which improved Alibaba’s Double‑11 transaction conversion rates by over 5% overall and more than 25% for optimal‑product selection scenarios.
This article reviews recent research on e‑commerce search, covering transformer‑based complementary rankings, Alibaba's cognitive concept net and its extension, joint deep retrieval with product quantization, personalized semantic retrieval, multi‑granularity deep semantic retrieval, and graph‑attention networks for long‑tail shop search.
After a decade of rapid growth, JD.com’s information system now handles procurement, sales, and inventory across a horizontally and vertically split architecture, tackling challenges of service decoupling, efficient communication, data consistency, and governance, while outlining its vision, principles, implementation details, and stability‑enhancing governance practices.
This article presents an end‑to‑end solution that leverages graph neural networks and dynamic bipartite graph construction to detect payment fraud in eBay's e‑commerce platform in real time, addressing traditional model limitations, graph latency challenges, and demonstrating superior performance over GBDT approaches.
This tutorial walks you through a real‑world example of constructing e‑commerce user profiles using Python, pandas, and matplotlib, covering data loading, preprocessing, feature engineering, behavior tagging, activity analysis, and RFM segmentation with clear code snippets and visualizations.
DeWu’s e‑commerce platform consolidated dozens of small‑scale transaction scenes into a universal personalized recommendation system by adopting a user‑to‑item DSSM dual‑tower model with unified sampling, category‑aware negative mining, cosine‑normalized embeddings, and real‑time serving, boosting click‑through rates by over 10% across all scenarios.
Lazada’s Southeast Asian e‑commerce app achieved major performance gains by reorganizing startup tasks, merging launch pages, implementing full‑link dynamic prefetch, incremental cart updates, checkout pre‑rendering, and request merging, resulting in up to 250 ms faster launches and significant reductions in network latency across Android and iOS.
This article examines the step‑by‑step evolution of the recommendation framework used by a major e‑commerce service, detailing the shortcomings of the initial V1.0 design, the vertical and modular refinements introduced in V2.0, and the dynamic configuration, pipeline, and service‑oriented enhancements implemented in V3.0 to improve scalability, stability, and fine‑grained experimentation.
The article outlines the design of a cloud‑based shopping cart system, detailing its three‑layer application architecture, dual cluster deployment, distributed technical stack, performance and reliability goals, three‑tier caching strategy, and additional features such as anti‑scalper measures and personnel identification.
The article explores the rapid growth of China's digital economy, the escalating risk of data leaks, and how digital watermarking—across images, text, and databases—can be employed to trace leakage sources, protect e‑commerce data, and address practical challenges in security implementations.
This article chronicles the evolution of Taobao’s open‑card technology from page‑level mini‑programs to the new module‑level “widget” system, detailing its architecture, developer workflow, real‑world Double 11 deployments, technical challenges, and future roadmap for enhancing e‑commerce front‑end experiences.
The January 8th Shanghai salon, co‑hosted by DeWu Technology and Juejin, gathered e‑commerce experts to discuss peak‑season stability, emphasizing balanced mid‑platform development, link governance, rapid recovery, cross‑functional collaboration, and architecture governance through global visibility, measurement, and control of services and technical environments.
A group of e‑commerce engineers share their real‑world experiences on selecting technologies, designing architectures, handling rapid growth, and avoiding common mistakes when building a new online store from scratch, offering practical guidance on infrastructure, DevOps, and team organization.
This article presents a comprehensive overview of building and applying product knowledge graphs for e‑commerce, covering background, recent advances in graph neural network‑based entity alignment, online prediction pipelines, data construction, evaluation metrics, attribute extraction, and future research directions.
The article explains what a flash‑sale (秒杀) is, outlines the severe technical challenges it creates for e‑commerce platforms such as massive concurrent traffic, database load and bandwidth spikes, and then presents concrete backend strategies and architectural designs to handle these issues efficiently.
The article summarizes JD.com Vice President Bao Yongjun's presentation on applying ultra‑large‑scale graph technology to e‑commerce, covering data foundations, recommendation and fraud detection use cases, technical challenges, the Galileo graph engine, and future AI computing development directions such as chips, auto‑learning, application layers, and privacy protection.
This article details Shopee's technical innovations—from logistics scheduling, address standardization, and fresh‑food expiry management to live‑streaming infrastructure, search scaling, real‑time data synchronization, chatbot analytics, and high‑throughput payment and order systems—showcasing how engineering breakthroughs power its 11.11 mega‑sale.
By leveraging existing middle‑platform services and a lightweight, configurable discount component, Xianyu rapidly built a full‑scale Double 11 marketing discount system—covering merchant recruitment, unified discount calculation, pre‑heat visuals, and robust load‑testing—delivering a stable promotion platform that can be reused for future campaigns.
This talk presents the design and implementation of 58’s commercial recruitment recommendation system, covering the characteristics of the app’s recommendation scenario, system architecture, region‑based and behavior‑based recall methods, and coarse‑ and fine‑ranking models with various optimizations and future directions.
This talk presents DAMO Academy’s end‑to‑end NLG pipeline for e‑commerce, covering the XGeneration content‑reproduction system, controllable short‑text generation models (PKM, PSCN), KG‑to‑text conversion, metaphor generation, and new‑media extensions such as short‑video script creation and intelligent video editing, together with experimental results and practical applications.
The talk demonstrates how to apply Domain‑Driven Design and microservice architecture to an e‑commerce voucher platform by defining problem spaces and bounded contexts, prioritizing boundaries over aggregates, using layered‑on‑demand infrastructure, and following SOLID principles to achieve clear, maintainable services.
The presentation explains how JD.com leverages large‑scale text mining and NLP techniques—including data cleaning, multi‑level labeling, sentiment classification with models such as TextCNN, RoBERTa, and USE—to transform unstructured customer feedback into actionable product insights across various e‑commerce scenarios.
This article details the evolution of Vivo's e‑commerce product system from a monolithic design to a modular, high‑performance backend, describing architectural changes, challenges such as stability, scalability, and data consistency, and the technical solutions implemented to address them.
This article explains the concept and core steps of funnel analysis, demonstrates its application with a detailed Taobao reverse‑logistics case, identifies key metrics and pain points, and offers data‑driven recommendations to improve conversion and on‑site pickup rates.
This tutorial demonstrates how to analyze an e‑commerce search page, use Selenium to automate browsing, extract product details across multiple pages, and store the collected data into a MySQL database, providing complete Python code and step‑by‑step explanations.
Meituan Search tackles the difficulty of ranking items from diverse business lines by introducing a five‑tower mixed architecture, group‑lasso and feature‑gate selection, a probabilistic graph model, and a joint block‑order/size predictor, achieving notable offline NDCG gains and online CTR and purchase‑rate improvements.
This article explains how to build an e‑commerce SaaS shop‑decoration system from scratch, covering overall design, template‑protocol structure, the three core designer tools—component selector, renderer, and property editor—and considerations for cross‑platform, dynamic components, user experience, ecosystem, and productization.
This article examines how thoughtfully crafted main visuals influence user engagement and sales in e‑commerce campaigns, presenting four case studies from the “Super Welfare Day” series that illustrate design background, strategy, visual style, implementation, and measurable results such as an 85.2% GMV lift.
The article explains the master‑sub order model used in e‑commerce, defines master orders as collections of sub‑orders and sub‑orders as single‑SKU transactions, evaluates design options for goods, services, and fees, reviews the current “得物” single‑SKU system, and proposes a scalable multi‑sub‑order, multi‑master‑order architecture to support combined payments, multi‑warehouse fulfillment, and complex purchase scenarios.
This article presents a comprehensive technical overview of JD.com’s e‑commerce recommendation and advertising systems, covering business scenarios, recall optimizations (profile and similarity‑based), multi‑task ranking improvements, sample weighting, multi‑model ensembles, PID‑based CPC control, conversion‑delay modeling, and the achieved performance gains and future research plans.
This article introduces a lightweight Spring Boot‑based mini Tmall e‑commerce project, detailing its core features—from user registration and product browsing to cart, order processing, and admin management—along with the technology stack, UI screenshots, and a four‑step deployment guide for developers.
This article presents Alibaba's AliMe team’s year‑long progress on knowledge graphs, covering the basics of knowledge graphs, domain‑specific and multi‑modal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and key takeaways for future research.
This article presents Alibaba's AliMe team's year‑long progress on knowledge graph research, covering the fundamentals of knowledge graphs, domain and multimodal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and insights on future directions.
This article curates a collection of popular open‑source e‑commerce projects on GitHub and Gitee, covering front‑end, back‑end, micro‑service, mobile, and cloud‑native architectures, with detailed technology stacks, core features, and direct repository links for developers to explore and adopt.
The Vivo online mall introduces a multi‑dimensional pricing capability matrix and floor‑price monitoring system that flags or blocks orders when combined promotions push SKU prices below predefined thresholds, providing pre‑event configuration checks, real‑time alerts during sales, and post‑event analysis to prevent loss and price‑hunting incidents.
This article presents a practical study on modeling real‑time positive and negative feedback sequences and applying multi‑objective optimization in the re‑ranking stage of Taobao Live, detailing system architecture, feature engineering, loss design, experimental results, and future research directions.
This article explains the theory and practice of handling SKU (Stock Keeping Unit) in e‑commerce platforms, covering product creation, cart selection, Cartesian‑product based SKU generation, adjacency‑matrix and set‑theory approaches, and provides complete JavaScript implementations for front‑end developers.
Xianyu built a custom SPU system and data pipeline that cleans Alibaba’s raw SPU data, defines key, binding, sales and product attributes, stores enriched records in MySQL, syncs to OpenSearch, and supports diverse business scenarios such as inspection, search publishing, and worry‑free purchase.
The team built a whitelist‑based time‑travel module that overrides the system clock for selected operators, letting them preview future promotional prices in Vivo’s e‑commerce flow without creating real orders, by propagating an openId context through Dubbo filters and replacing System.currentTimeMillis() with a custom TimeTravelUtil.
This article explains the design and implementation of a modular, configurable pricing engine that handles real‑time and simulated price calculations for complex e‑commerce promotions, addressing rule fragmentation, extensibility, priority conflicts, and transaction‑level precision while ensuring high performance and reliability.
This article introduces the open‑source WeChat mini‑program mall (Java version), detailing its technical stack, development plan, project structure, core features such as member and order management, SMS service integration, and step‑by‑step installation and deployment instructions for both backend and frontend components.
This article explains how the Dolphin system implements floor‑based custom layouts on the client side, detailing data request handling, recursive layout initialization, content updating, error‑retry mechanisms, and future enhancements for the e‑commerce channel page.
The article details Vivo Mall’s new promotion pricing engine, which replaces duplicated site‑level logic with a layered model (product, shop, platform) that supports configurable stacking, processes discounts through three calculation stages, and uses a meta‑driven rule engine to flexibly interpret JSON promotion templates, ensuring extensible, stable pricing functionality.
The article describes Alibaba's intelligent data assistant for live streaming, detailing streamer needs, the product's real‑time and post‑live reporting features, AI‑driven insights, the underlying big‑data architecture, and a rigorous experiment that demonstrates significant sales uplift and efficiency gains.
This article provides a comprehensive technical overview of Youzan's Cost‑Per‑Sale (CPS) system, detailing its basic concepts, business models, implementation architecture, attribution mechanisms, billing rules, profit‑sharing process, testing challenges, and future expansion across multiple e‑commerce platforms.
The article describes a full‑traffic A/B testing framework for Xianyu that hashes seller IDs to create exclusive experiment and control groups, ensuring each seller sees only one strategy, and demonstrates that a chat‑incentive for new or churned sellers boosted chat exposure by 22 % and modestly improved overall buyer‑seller metrics without harming transaction efficiency.
This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.
The study presents an asynchronous chat‑assistant for second‑hand e‑commerce that uses intent recognition, price‑strength data, and filtered messaging to generate friendly bargaining and greeting scripts, achieving a 4 % rise in reply and conversion rates, and outlines plans to add more intents and richer product extraction.
The article details Youzan’s Return Shipping Fee insurance service, explaining its consumer and merchant benefits, end‑to‑end workflow, integrated risk‑control mechanisms, insurance‑product specifications, and a layered technical architecture—business, domain, component, and dependency services built with domain‑driven design—to enable scalable, reusable e‑commerce insurance solutions.
The article outlines a complete e‑commerce payment architecture, detailing the transaction core and the extensive payment orchestration layer, and provides numerous diagrams that illustrate how each component interacts to achieve scalable, reliable, and secure online payment processing.
This article presents a comprehensive overview of Alibaba 1688's user‑growth strategy, detailing lifecycle segmentation, budget‑constrained installation optimization, intelligent red‑packet allocation, smart push mechanisms, information‑flow advertising, and the deep‑learning‑driven search pipeline that together power the platform's growth engine.
This article explains the complete technical design of an e‑commerce coupon system, covering data models, product‑page exposure, coupon claiming rules, high‑concurrency caching, MQ‑driven expiration handling, order‑time coupon calculation, redemption flow, and search‑based coupon indexing.
The article explains how deep‑learning models, particularly convolutional neural networks with token‑level padding, kernel size three, and max‑pooling, can automatically classify e‑commerce product reviews into eight sentiment categories, offering scalable insight for decision‑making and paving the way for recommendation, QA, and risk‑assessment applications.
This article explains the role of order systems in traditional e‑commerce enterprises, outlines their main functional modules and business architecture, describes forward and reverse order processes, inventory deduction strategies, state‑machine design, and discusses future development challenges and the need for a unified order center.
This article explains how high‑throughput e‑commerce order processing can suffer from inventory oversell when using a single distributed lock, analyzes the lock’s concurrency bottleneck, and proposes a segment‑locking optimization—splitting stock into multiple lock keys—to dramatically increase parallelism while addressing edge‑case pitfalls.
This article examines the fundamentals of e‑commerce platforms, classifies platform types, compares self‑built versus third‑party solutions, outlines overall architectural design, and evaluates implementation options such as software outsourcing and long‑term partnership, providing a strategic roadmap for enterprises.
This article reviews the evolution of Vivo's e‑commerce promotion system, detailing why the original monolithic architecture failed, how a separate, highly extensible promotion service was designed, the technical challenges of scalability, high concurrency, and stability, and the practical lessons learned from Redis and hotspot key issues.
The article details Baidu’s 2020‑built commodity promotion system for Baijiahao and live‑stream e‑commerce, linking merchants with authors/streamers through CPS billing, three user interfaces and five core services, and highlights technical choices such as dynamic‑library tracking, asynchronous batch writes, and a high‑cohesion, low‑coupling architecture requiring cross‑team collaboration.
This article provides a comprehensive overview of e‑commerce order system roles, core modules, workflow design, process engine, state machine, and future architectural evolution, offering practical guidance for building scalable and maintainable order management platforms.
This article explains the essential concepts, functional architecture, and detailed design of an e‑commerce product center, covering SPU/SKU definitions, front‑ and back‑end categories, attribute types, CRUD operations, and specific features such as product publishing, review, unpublishing, editing, and category management.
This article explains JD.com's order transfer process, inventory hierarchy, support relationships, order transfer and planning systems, fulfillment workflow, and risk control mechanisms, illustrating how millions of orders are allocated, scheduled, and executed across multiple warehouses and channels.
This article explains JD.com's end‑to‑end order fulfillment process, the concepts of 211/411 delivery promises, the mechanisms behind order splitting by warehouse, merchant and payment dimensions, and how monetary discounts are proportionally allocated across SKUs.
This article explains JD.com's order fulfillment process, including the OFC system, delivery promises like 211 and 411, appointment scheduling, order splitting across warehouses, merchants and payment methods, and the amount‑splitting mechanism that allocates discounts and fees per SKU.
The article explains CPS (Cost Per Sale) as a performance‑based advertising model used by e‑commerce platforms, describes how promoters earn commissions, buyers receive hidden coupons, and outlines two contrasting coupon‑community operation methods—manual curation and automated blasting—while highlighting their benefits, drawbacks, and the broader traffic‑driven nature of the CPS ecosystem.
The paper describes Xianyu’s new relevance‑matching pipeline—integrating basic, text‑matching, semantic (BERT‑based dual‑tower), multimodal, and click‑graph features and fusing them with a GBDT model—which boosts search DCG@10 by 6.5 %, query satisfaction by 24 % and click interaction by over 20 % while outlining future enhancements for finer attribute matching and richer structured data.
