Movie-Recommender-System---SVD-CUR-Collaborative-Filtering

This directory contains code for implementation of Collaborative filtering, SVD, CUR. The complement code is implemented in Python and requires python3 for execution

The packages used in the execution of the program are:

• numpy
• math
• operator
• time

The code is divided into 9 functions as follows:

1. collaborative_filtering_func: Function that carries out collaborative filtering algorithm
2. find_similarity: It gives the similarity between two vectors
3. svd_func: Function to initiate the SVD algorithm
4. get_new_VT: This function shrinks the size of VT when 90% energy is being maintained in SVD algorithm
5. predict: This function is called in svd_func function. It carries out the task of predicting the testing data
6. cur_func: This function initiates the CUR algorithm
7. select_random_rows: This function is used to select random rows from a given matrix where each row can have a probability of being selected.
8. find_U_and_rmse: This function finds the U matrix and also calculates RMSE, Precision and Spearman Rank Correalation
9. get_top_k_movies: This function is used to get the top most rated movies. Its used in finding the Precision

Results obtained:

Collaborative without baseline approach:

• RMSE: 0.0768
• Precision on top 50: 0.76
• Spearman Rank Correlation: 0.999704867798
• Time taken: 36.071 secs

Collaborative with baseline approach:

• RMSE: 0.0802659721
• Precision on top 50: 0.76
• Spearman Rank Correlation: 0.999677846314
• Time taken: 34.13582 secs

SVD:

• RMSE: 0.011385705026
• Precision on top 50: 0.12
• Spearman Rank Correlation: 0.999999992222
• Time taken: 8.547 secs

SVD with 90% retained energy:

• RMSE: 0.0113993709
• Precision on top 50: 0.12
• Spearman Rank Correlation: 0.999999992203
• Time taken: 3.1909 secs