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A recommender system for Amazon products implemented on pySpark.

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Product Recommender Engine

Teams:

Lakshmi Udupa (800956319)

Shreyas Subramanya Bhat (800958406)

Input files:

Cosine-similarity method:

ratings2.csv

Alternating least squares method:

ratings_als.csv
metadata.csv
Single_user_rating.txt

Steps to execute:

Cosine-similarity method: To execute recommenderEngine.py

  1. Place the input file in the hdfs

     sudo hdfs dfs -put <file_name> /user/root/
    
  2. To run the recommenderEngine.py

     spark-submit --driver-memory 2g recommenderEngine.py ratings2.csv 9994 0439893577 > test.out
    
  3. To run the cos_similarity.py

     spark-submit --driver-memory 2g cos_similarity.py ratings.csv meta_data.csv 5594 > output.out 
    

Alternating least square method: To execute recommenderEngine_ALS.py

  1. Place the input file in the hdfs

     sudo hdfs dfs -put <file_name> /user/root/
    
  2. To run the recommenderEngine.py

     spark-submit --driver-memory 2g recommenderEngine_ALS.py new_data.csv meta_data.csv personalRatings.txt > output.out
    

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