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GPGPU Parallel User-User Collaborative Filtering System in CUDA C

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parallel-recommenders

APP Recommender Systems Project

Compilation Instructions

make

Usage

./parallel-recommenders <path-to-rating-file> <path-to-movie-file> <delimiter> <user-id-file> <n>

  • specify path to a valid ratings file.
  • specify path to a valid movies file.
  • delimiter used by these files.
  • sample valid data files here.
  • specify path to file with IDs of the users we need to make recommendations for.
  • n is the number of top recommendations to return.

For example, ./parallel_recommenders data/ratings-20k.dat data/movies.dat :: user-ids.txt 10 yields..

/*** Console Output ***/
 
 Normalizing Ratings took 0.010721 seconds.

 Computing UU Similarity - Gold
 Took 169.373 seconds.

 Calculating Top-10 Recommendations for 10 users - Gold.
 Took 6.86971 seconds for 10 users.

 Computing UU Similarity - Parallel
 Took 4.26382 seconds.
 Error %age: 0.32
 Similarity Kernel Result Verification: SUCCESS

 Calculating Top-10 Recommendations for 10 users - Parallel.
 Took 0.035726 seconds for 10 users.

 Total Kernel Time: 4.299544

 Similarity Speedup: 39.723209
 Recommendation Speedup: 192.288895
 Total Speedup: 40.990917
 
 Error %age: 0
 Recommendations Kernel Result Verification: SUCCESS

** Recommendations Output File "kernelRecommendations_3.csv" **

Movie Id Movie Title Score
52694 Mr. Bean's Holiday (2007) 6.358
26246 Johnny Got His Gun (1971) 6.179
6255 Bible, The (a.k.a. Bible... In the Beginning, The) (1966) 5.990
6237 Glenn Miller Story, The (1953) 5.990
5960 Bad Influence (1990) 5.932
5389 Spirit 5.917
27509 Carolina (2005) 5.858
50005 Curse of the Golden Flower (Man cheng jin dai huang jin jia) (2006) 5.858
26122 Onibaba (1964) 5.858
34198 Russian Dolls (Les PoupĂŠes russes) (2005) 5.858

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GPGPU Parallel User-User Collaborative Filtering System in CUDA C

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