Practical Machine Learning: Innovations in Recommendation Paperback

Ted Dunning, Ellen Friedman

  • 出版商: O'Reilly
  • 出版日期: 2014-11-04
  • 定價: $726
  • 售價: 9.5$690
  • 貴賓價: 9.0$653
  • 語言: 英文
  • 頁數: 56
  • 裝訂: Paperback
  • ISBN: 1491915382
  • ISBN-13: 9781491915387
  • 相關分類: Machine Learning
  • 立即出貨 (庫存 < 3)

相關主題

商品描述

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.

Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You’ll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.

  • Understand the tradeoffs between simple and complex recommenders
  • Collect user data that tracks user actions—rather than their ratings
  • Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis
  • Use search technology to offer recommendations in real time, complete with item metadata
  • Watch the recommender in action with a music service example
  • Improve your recommender with dithering, multimodal recommendation, and other techniques

商品描述(中文翻譯)

建立一個簡單但功能強大的推薦系統比你想像的要容易得多。這份報告適合各種專業水平的人,解釋了使機器學習在商業生產環境中實用的創新技術,並展示了即使是小型開發團隊也可以設計出有效的大型推薦系統。

Apache Mahout 的貢獻者 Ted Dunning 和 Ellen Friedman 將帶領你通過仔細簡化的設計。你將學習如何收集正確的數據,使用 Mahout 函式庫中的算法進行分析,然後使用搜索技術(如 Apache Solr 或 Elasticsearch)輕鬆部署推薦系統。這種強大而有效的組合可以在離線學習並實時提供快速的推薦。

本書內容包括:
- 理解簡單和複雜推薦系統之間的權衡
- 收集追蹤用戶行為而非評分的用戶數據
- 使用 Mahout 進行共現分析,根據其他用戶的行為預測用戶的需求
- 使用搜索技術實時提供推薦,包括項目元數據
- 通過音樂服務示例觀察推薦系統的運作
- 使用抖動、多模態推薦和其他技術改進推薦系統