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.
建立一个简单但功能强大的推荐系统是比你想象的要容易得多。平易近人为所有级别的专门知识，本报告解释了使机器学习实际用于商业生产设置的创新 — — 和阐释了即使一个小型开发团队可以设计一个有效的大规模推荐系统。
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.
Apache Mahout 委托人 Ted 邓宁和埃伦 · 弗里德曼为你走过了依赖于精心的简化设计。您将学习如何收集正确的数据、 分析与算法从 Mahout 库中，然后轻松地部署推荐使用搜索技术如 Apache Solr 或 Elasticsearch。强大的和有效的这个高效的组合离线学习，并实时提供快速反应的建议。
Understand the tradeoffs between simple and complex recommenders
了解简单的和复杂的推荐系统之间的权衡
Collect user data that tracks user actions—rather than their ratings
收集跟踪用户操作的用户数据 — — 而不是它们的评级
Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis
预测用户想要什么基于行为的其他人，使用 Mahoutfor 共生分析
Use search technology to offer recommendations in real time, complete with item metadata
使用搜索技术提供建议在真正的时间，完成使用项元数据
Watch the recommender in action with a music service example
在行动上与音乐服务示例推荐的手表
Improve your recommender with dithering, multimodal recommendation, and other techniques
提高您的推荐人与抖动、 多式联运的建议和其他技术
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