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The implementation of Recommendation Algorithms based on Hadoop

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RecommendEngine

Implement the Item-based Recommendation Algorithm and calculate the similarity of users and items.

Notice : I'm sorry about that it is not efficient enough in this recommendation engine when calculating the similarity matrix and computing the prediction matrix. I have optimized this problem and the code will be shared soon.

Implementions

  • Item-based Recommendation Algorithm is the most commomly used algorithm in Recommendation System, you can refer to 《Mahout in Action》Chapter1.6 “Distributing recommendation computations” for more details.
  • I calculated the Item-Similarity and User-Similarity by reading paper “Empirical Analysis of Predictive Algorithms for Collaborative Filtering”, by John S.Breese.
  • I also implement a CRON for RecommendEngine so that the recently-records can be fetched from MySQL to HDFS and after accomplish the computation tasks, the recommended result will be loaded into MySQL periodically.

DataSets

  • I got MovieLens DataSets from here.

Running Environment

  • Ubuntu 14.04
  • JDK 1.7.0_75
  • Hadoop 1.2.1
  • Sqoop 1.3.0

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The implementation of Recommendation Algorithms based on Hadoop

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