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Practical Machine Learning - Innovations in Recommendation (Paperback)
Loot Price: R373
Discovery Miles 3 730
You Save: R128
(26%)
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Practical Machine Learning - Innovations in Recommendation (Paperback)
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List price R501
Loot Price R373
Discovery Miles 3 730
You Save R128 (26%)
Expected to ship within 12 - 17 working days
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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
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