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Implemented SVD, SVD++ and timeSVD++. Can be used on the netflix data to make predictions. Data can be downloaded from https://minnow.noip.me/~jzhou/courses/S15.BigData/assignments/pmd-project.tar.gz
macklin-fluehr/timeSVDplusplus
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Team Members: 5120309085 5120309016 5120309005 Folders: SVD -- Implementation of the latent factor model with biasis. SVD++ -- Extension of SVD, which takes implicit feedback into consideration. timeSVD++ -- Extension of SVD++, which further takes temporal dynamics into consideration. And that's what we use. More details can be found in the report and the codes. Performance on the site provided for testing: SVD -- approximately 0.91564 SVD++ -- approximately 0.91133 timeSVD++ -- approximately 0.904791 Files: tool_used.txt -- List the tools we used. 5120309085_5120309016_5120309005.txt -- The rating predictions made for the test set. SVD.h: Headerfile of the class SVD. SVD.cpp: Implementations of the class SVD. main.cpp: Generate "train.txt" for training and "cross.txt" for cross validation and generate predictions for "test.txt". run.sh: Excutable file generated by main.cpp. Makefile: makefile Quick Start: Move "training.txt" and "test.txt" into this folder. Make. Run run.sh. One example of timeSVD++: linzebing@ufo:~/MMDS/timeSVD++$ make g++ -std=c++0x -c main.cpp main.cpp: In function ‘int main()’: main.cpp:28:29: warning: format ‘%s’ expects argument of type ‘char*’, but argument 3 has type ‘char (*)[2048]’ [-Wformat=] while (fscanf(fp,"%s",&s)!=EOF) { ^ g++ main.o -o run.sh linzebing@ufo:~/MMDS/timeSVD++$ ./run.sh test_Rmse in step 0: 0.862396 test_Rmse in step 1: 0.837672 test_Rmse in step 2: 0.826663 test_Rmse in step 3: 0.819765 test_Rmse in step 4: 0.814822 test_Rmse in step 5: 0.811096 test_Rmse in step 6: 0.80822 test_Rmse in step 7: 0.805967 test_Rmse in step 8: 0.804171 test_Rmse in step 9: 0.802715 test_Rmse in step 10: 0.801514 test_Rmse in step 11: 0.800517 test_Rmse in step 12: 0.799684 test_Rmse in step 13: 0.798985 test_Rmse in step 14: 0.798395 test_Rmse in step 15: 0.797893 test_Rmse in step 16: 0.797463 test_Rmse in step 17: 0.797094 test_Rmse in step 18: 0.796776 test_Rmse in step 19: 0.796501 test_Rmse in step 20: 0.796262 test_Rmse in step 21: 0.796055 test_Rmse in step 22: 0.795874 test_Rmse in step 23: 0.795716 test_Rmse in step 24: 0.795578 test_Rmse in step 25: 0.795457 test_Rmse in step 26: 0.795351 test_Rmse in step 27: 0.795258 test_Rmse in step 28: 0.795176 test_Rmse in step 29: 0.795103 test_Rmse in step 30: 0.795038 test_Rmse in step 31: 0.79498 test_Rmse in step 32: 0.794928 test_Rmse in step 33: 0.794882
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Implemented SVD, SVD++ and timeSVD++. Can be used on the netflix data to make predictions. Data can be downloaded from https://minnow.noip.me/~jzhou/courses/S15.BigData/assignments/pmd-project.tar.gz
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