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Examples of Collaborative Filtering based Recommendation Systems

#make necesarry imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
import numpy as np
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import correlation, cosine
import ipywidgets as widgets
from IPython.display import display, clear_output
from sklearn.metrics import pairwise_distances
from sklearn.metrics import mean_squared_error
from math import sqrt
import sys, os
from contextlib import contextmanager
#M is user-item ratings matrix where ratings are integers from 1-10
M = np.asarray([[3,7,4,9,9,7], 
                [7,0,5,3,8,8],
               [7,5,5,0,8,4],
               [5,6,8,5,9,8],
               [5,8,8,8,10,9],
               [7,7,0,4,7,8]])
M=pd.DataFrame(M)

#declaring k,metric as global which can be changed by the user later
global k,metric
k=4
metric='cosine' #can be changed to 'correlation' for Pearson correlation similaries
M

User-based Recommendation Systems