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Implementation of movie recommendation system using C
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Dataset added files Dec 20, 2017
LICENSE
README.md
kmeans.c
kmeans.h k means header file Dec 20, 2017
matrix_normalization.c
matrix_normalization.h matrix normalization header file Dec 20, 2017
pearsons.c
pearsons.h pearsons header file Dec 20, 2017
predictions.c
predictions.h
recommender.c recommender file Dec 20, 2017
recommender.h
sorting.c
sorting.h
ui.c user interface Dec 20, 2017
utility_matrix.c
utility_matrix.h header file for utility matrix Dec 20, 2017

README.md

Movie-Recommendation-System

Implementation of movie recommendation system using Collaborative Filtering Technique using C language.

Algorithm:

  1. Creation of utility matrix of ratings between users and movies.
  2. Finding similarity of new user (or concerned user) with other users using Centered Cosine Similarity (Pearson's Correlation).
  3. Clustering of users based on similarity using k means clustering.
  4. Prediction of movies using Collaborative Filtering technique (Low Rank Matrix Factorization) based on clusters obtained in step 3.
  5. Top 10 movies printed on command line.

Instructions to run this system:

  1. Open recommender.c and correct all the paths. (on line 20,55,56,57 and 63).
  2. Open ui.c and correct all the paths. (on line 15, 41, 64, 67, 68).
  3. Open command line and locate the directory where source code is stored and type: gcc ui.c kmeans.c matrix_normalization.c pearsons.c predictions.c recommender.c sorting.c utility_matrix.c
  4. Type a on command line to execute the program.

Output:

https://imgur.com/a/dk3IY

RMS Error: 0.865843

Authors and Maintainers

  1. Shubham Bhatnagar (https://github.com/shubham-bhatnagar)
  2. Udhav Sharma (https://github.com/UdhavSharma)

If you encounter any problems, please contact directly or post it in issues.

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