By Ted Dunning,Ellen Friedman
ISBN-10: 1491915382
ISBN-13: 9781491915387
Building an easy yet robust suggestion approach is far more uncomplicated than you think that. Approachable for all degrees of craftsmanship, this document explains techniques that make desktop studying sensible for enterprise creation settings—and demonstrates how even a small-scale improvement group can layout an efficient large-scale advice system.
Apache Mahout committers Ted Dunning and Ellen Friedman stroll you thru a layout that depends upon cautious simplification. You’ll the way to acquire the correct information, study it with an set of rules from the Mahout library, after which simply installation the recommender utilizing seek know-how, reminiscent of Apache Solr or Elasticsearch. robust and powerful, this effective mixture does studying offline and gives you speedy reaction strategies in actual time.
- Understand the tradeoffs among basic and intricate recommenders
- Collect person facts that tracks consumer actions—rather than their ratings
- Predict what a consumer desires in response to habit by means of others, utilizing Mahoutfor co-occurrence analysis
- Use seek know-how to provide techniques in genuine time, entire with merchandise metadata
- Watch the recommender in motion with a tune carrier example
- Improve your recommender with dithering, multimodal suggestion, and different techniques
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Practical Machine Learning: Innovations in Recommendation by Ted Dunning,Ellen Friedman
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