Beyond chatting, video streaming, and online shopping, the internet offers powerful academic tools, and this guide lists essential online databases and search engines that let students, researchers, and librarians efficiently locate valuable mathematics journals, articles, and e‑books.
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.
The article reports on Ant Financial’s participation in ICML 2019, highlighting a finance‑focused AI workshop and summarizing three of its cutting‑edge research papers on adversarial reinforcement‑learning recommendation, distributional gradient temporal‑difference learning, and particle‑flow Bayesian inference.
The paper presents a guarantee‑based, distributed allocation framework for Alibaba’s off‑site brand contract ads that extends the SHALE algorithm with effect‑driven objectives and explicit over‑allocation constraints, solves dual variables via coordinate descent, and employs adaptive probability‑based pacing to meet volume guarantees while significantly boosting average CTR.
This article explores how the wildly successful game PUBG illustrates various internet business models, explains the components of a business model, examines common ecosystem and freemium models, details advertising and revenue streams of 58.com, and shares design strategies for large‑scale B‑end products.
This article presents a comprehensive approach that combines causal inference, large language models, and retrieval‑augmented generation to automate e‑commerce price recommendation, detailing the three‑step workflow, challenges across product categories, the RAG architecture, process‑reward‑guided tree search, reinforcement learning refinements, and experimental results showing significant accuracy and speed improvements.
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.
Alibaba’s research team secured three NeurIPS 2022 papers—introducing an Adaptive Parameter Generation network that boosts click‑through rates and revenue, a tuning‑free Global Batch Gradient Aggregation method that speeds recommendation model training by 2.4×, and a Sustainable Online Reinforcement Learning framework that outperforms existing auto‑bidding strategies.
The article details the development and deployment of LORE, a large generative model that reshapes e‑commerce search relevance by combining knowledge injection, chain‑of‑thought reasoning, and multimodal alignment, achieving simultaneous improvements in user experience and revenue metrics.
This article investigates the challenge of generating concise e‑commerce product short titles by formalizing it as a constrained text‑summarization task, proposes a Multi‑Source Pointer Network that leverages both title and background knowledge encoders, and demonstrates its superiority through extensive offline and online experiments.
This article analyzes the challenges of traditional product selection on Taobao and presents an LLM‑driven solution that combines multi‑round online search, DeepSearch vs. WideSearch strategies, sample construction, SFT and RL training, and shows experimental results that improve relevance, diversity, and efficiency of the selected product set.
This article explains how modern internet companies design payment systems, covering product classifications, module capabilities, typical business processes, and reference architectures to help developers and product teams build secure, scalable payment solutions.
This article presents the BiCB (Binary Constrained Bidding) algorithm, a lightweight auto‑bidding solution for live advertising that combines optimal bidding formulas derived from linear‑programming dual analysis with traffic‑prediction statistics to achieve near‑optimal performance under budget and CPC constraints.
AdaCalib is a posterior‑guided feature‑adaptive calibration model for online advertising that learns per‑feature piecewise‑linear calibration functions via a deep neural network with adaptive bucketing, improving probability estimates and ranking, achieving lower Field‑RCE, higher AUC, and a 5% CVR lift in live tests.
This article walks you through Hulu's 2021 campus recruitment update, explains the online programming test schedule and format, shares interview tips, and provides detailed statements, sample I/O, and solution analyses for four algorithmic problems covering probability, array maxima, maze navigation, and segment‑tree optimization.
This article describes a QCon talk that combines causal inference with large language models to build a retrieval‑augmented generation pricing system for e‑commerce, detailing the three‑step algorithm, LLM‑driven modeling challenges, process‑reward tree search, reinforcement‑learning fine‑tuning, and experimental gains in accuracy and speed.
This article breaks down the monthly expenses hackers incur for tools, services, and infrastructure, then reveals how much they can earn through ransomware, malicious certificates, fake antivirus, IP reputation abuse, web shells, and user data trading, highlighting the lucrative yet risky nature of cybercrime.
The article details how attackers exploit premium phone numbers in Instagram, Google, and Microsoft’s two‑factor authentication to generate automated voice calls, earn money per call, and how the vulnerabilities were reported, mitigated, and rewarded through bug‑bounty programs.
This article describes Meituan's delivery advertising system's shift to intelligent compute allocation, detailing the business challenges, the four‑element framework for dynamic resource distribution, the optimal tier decision process, system stability mechanisms, experimental results, and future research directions.
At KDD 2021, Alibaba Mama presented six papers that introduced a unified constrained‑bidding solution, a deep‑learnable auction mechanism, real‑negative training for delayed‑feedback CVR, a contextual‑bandit advertising strategy recommender, a multi‑agent cooperative bidding game, and an uncertainty‑aware exploration model, all of which have been deployed to boost platform revenue and advertiser performance.
