This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to * Use the scientific method to evaluate hypotheses using controlled experiments * Define key metrics and ideally an Overall Evaluation Criterion * Test for trustworthiness of the results and alert experimenters to violated assumptions * Build a scalable platform that lowers the marginal cost of experiments close to zero * Avoid pitfalls like carryover effects and Twyman's law * Understand how statistical issues play out in practice.