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 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.
By deploying large‑scale visual AI—including a ResNet‑101 classifier, ArcFace‑trained matching features, clustering‑based sub‑category refinement, and product‑level image indexing—Xianyu’s marketplace dramatically improves image quality, removes duplicates, enhances search relevance and feed diversity, and filters non‑compliant content.
The Xianyu pricing guidance system blends new‑product market values with depreciation factors derived from usage, condition and category attributes—extracted via real‑time text mining and image analysis—to recommend dynamic price ranges adjusted for supply‑demand and seller urgency, currently covering 60% of listings with over 65% overall accuracy.
The article describes a supply‑demand modeling framework for the idle second‑hand market that extracts and structures product attributes, builds a decision‑tree‑based index from price, inventory, search‑hotspot and demand‑activation sub‑models, and uses the index to optimize category allocation, boost scarce supply, and drive overall growth.
By converting Mercari’s product titles, descriptions, and categorical data into TF‑IDF vectors and embeddings, training MLP and GRU networks, and ensembling them with weighted averaging, the authors achieve a 0.3873 RMSLE—matching the competition’s top score—and demonstrate the power of text‑only price prediction for C2C marketplaces.
The paper introduces an explainable LLM framework (ELLM‑rele) that uses chain‑of‑thought reasoning and a multi‑dimensional knowledge distillation pipeline to compress large‑model relevance judgments into lightweight student models, achieving superior offline relevance scores and online click‑through and conversion improvements in Taobao’s search advertising.
Alibaba’s advertising team introduces semantic‑aware contrastive learning to pre‑train multimodal image‑text embeddings, integrates them via SimTier and MAKE into ID‑based CTR models, achieving up to 6.9% lift in Taobao display ad click‑through rates and improving long‑tail item performance.
Alibaba’s AI team introduced AtomoVideo, a diffusion‑based image‑to‑video generator enhanced by a training‑free Noise Rectification module that adds and corrects controlled noise to eliminate first‑frame errors, enabling merchants to automatically create high‑fidelity 4‑second 720p product videos with strong temporal consistency for e‑commerce advertising.
This paper proposes a two‑stage Potential Generalized Second Price auction for augmented ads, ranking guide ads by expected welfare from their linked second‑step ads, shifting billing to the second click to eliminate free‑riding, and demonstrates via offline and online experiments on Taobao that it boosts click‑through, revenue, and GMV while lowering CPC.
The Xiaohongshu anti‑fraud team combats sophisticated same‑group and crowdsourced reselling bots by ingesting real‑time transaction streams into a Nebula Graph, using multi‑hop sub‑graph sampling, label propagation, and modularity‑based community detection to identify suspicious clusters, update risk pools, and enforce personalized purchase‑limit rules.
The article details a high‑concurrency e‑commerce coupon system that separates creation and distribution, uses Redis‑Lua atomic stock deduction with asynchronous replenishment and coupon generation, employs a lightweight distributed‑transaction table, and adds bucket and batch optimizations to safely handle tens of thousands of TPS.
The article details vivo’s transition from a monolithic mall to a micro‑service transaction platform, outlining a multi‑tenant architecture with ShardingSphere‑sharded MySQL, Snowflake IDs, Elasticsearch search, configurable state machines, generic delayed tasks, Seata and local‑message distributed transactions, plus high‑availability safeguards, emphasizing pragmatic solution selection.
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.
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.
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 chronicles Vivo’s official mall transformation from a 2015 monolithic Java MVC system to a service‑oriented architecture with independent activity, product, coupon, and order services, later expanding to consignment, CPS, and promotion subsystems, and finally adding multi‑language, multi‑timezone, and multi‑region capabilities for global markets.
YouZan built a side‑car, real‑time loss‑prevention system that monitors business events and database binlogs, validates information flow and fund balance, enforces idempotency and manual‑change checks, and uses refund‑settlement‑cash‑out circuit‑breakers to automatically detect and stop financial loss, achieving successful fault prevention after six months.
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.
The paper presents an intelligent reach system for e‑commerce that automatically selects audience, timing, channel, welfare and creative content using user, content and decision models—including XGBoost churn predictions, NLP‑generated copy, Bayesian CTR estimation and linear‑programming optimization—resulting in a 17.4 % rise in paying users and a 5 % revenue boost over manual methods.