MapReduce implementations movie recommendation system collaborative filtering algorithm based on item

I uploaded the source code to see the project resource file

## MapReduce movie recommendation system to achieve ### Case Study

• Internet a movie review site, the main products include
  • Film introduction
  • Top Movies
  • Users of film scoring
  • Critics friends
  • Telecine & Tickets
  • Users looking | to see | seen the movie
  • You may also like (recommended)
• Users use the film scoring table to recommend the movie to the user, the user scoring table includes the following fields
  • userID-- user ID number
  • itemID-- film ID number
  • score-- score
    

Collaborative filtering algorithm based on article ###

• The establishment of co-occurrence matrix items
  
• User rating matrix to establish items
  
• Recommended matrix calculation results
  

### MapReduce achieve

• Program flow chart
  
• Java Class Description
  • Recommend.java-- main task launcher
  • Step1.java-- grouped by user, calculating a list of all items occurring in combination, to give the user of the article scoring matrix
  • Step2.java-- combined list of itemID counts which establish co-occurrence matrix
  • Step3.java-- rates of co-occurrence matrix and a transformation matrix, to facilitate subsequent processing
  • Step4_Update.java-- matrix multiplication multiplication section
  • Step4_Update2.java-- matrix multiplication addition section
  • Step5.java-- filter and sort the results
  • HDFSFile.java - HDFS path to the file-based operations
  • SortHashMap.java - HashMap class sorting
• Program output
  Step1: Step2: Step3_1 Rating Transition Matrix: Step3_2 transformation of co-occurrence matrix: Step4: Step5:
  
  
  
  
  
  
  
  
  
  
  

The project is based on ### Lian number into gold "is a sixth Hadoop application development real case" case provided to optimize