Isbn: 9781491915387 - practical machine learning: innovations in recommendation (8 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 48 pages. 8.75x6.00x0.25 inches. In Stock.

Zustand: New. pp. 56.

Zustand: New. In English.

Zustand: New. 2016. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware - 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 settingsand 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 actionsrather 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.…

Zustand: New. &Uumlber den AutorrnrnTed Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, ZooKeeper, and Drill projects and mentor for the Apache Storm, DataFu, Flink, and Optiq projects. .