Database Reference
In-Depth Information
org.apache.mahout.cf.taste.hadoop.item.RecommenderJob
-s SIMILARITY_PEARSON_CORRELATION
--input=/user/<USER NAME>/chapter15/input/
MovieRatings.csv
--output=/user/<USER NAME>/chapter15/output/
itemrecommendations
The item-based RecommenderJob, like the user-based recommender job
will take some time to run to completion. After the job successfully
completes, you can browse the results in the
/chapter12/output/
itemrecommendations
folder in HDFS.
Summary
Big data analytics, and more specifically data mining and predictive
analytics, represent the biggest and more potent parts of your big data
platform. Taking advantage of this data to gain new insights, identify
patterns, and bring out new and interesting information will provide a
competitive advantage for those businesses that take the leap.
HDInsight and the Mahout machine learning library make this area
approachable by abstracting away the complexity (mathematics and
statistics!) generally associated with data mining and predictive analytics.
Mahout provides implementations for clustering, classifying, and (as
demonstrated) generating recommendations using the concept of
collaborative filtering.
