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ItemBasedRecommendation.py
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ItemBasedRecommendation.py
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import random
import sys
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
userId=sys.argv[1]
userId=int(userId)
def dataFormatting(recommendedItems):
recommendedItems=set(recommendedItems)
recommendedItems=list(recommendedItems)
itemIterator=0
for r in recommendedItems:
itemIterator=itemIterator+1
if(r>56):
recommendedItems.pop(itemIterator-1)
random.shuffle(recommendedItems)
return recommendedItems
header = ['user_id', 'item_id', 'rating', 'timestamp']
dataset = pd.read_csv('user.data', sep='\t', names=header)
#print(dataset.head())
n_users = dataset.user_id.unique().shape[0]
n_items = dataset.item_id.unique().shape[0]
n_items = dataset['item_id'].max()
A = np.zeros((n_users, n_items), dtype=int)
for line in dataset.itertuples():
A[line[1]-1, line[2]-1] = line[3]
#print("Original rating matrix : ", A)
for i in range(len(A)):
for j in range(len(A[0])):
if A[i][j] >= 3:
A[i][j] = 1
else:
A[i][j] = 0
csr_sample = csr_matrix(A)
knn = NearestNeighbors(metric='cosine', algorithm='brute',
n_neighbors=3, n_jobs=-1)
knn.fit(csr_sample)
dataset_sort_des = dataset.sort_values(
['user_id', 'timestamp'], ascending=[True, False])
itemsLikedByUser = dataset_sort_des[dataset_sort_des['user_id'] == userId].item_id
itemsLikedByUser = itemsLikedByUser.tolist()
#print("Items liked by user: ", itemsLikedByUser)
distances1 = []
recommendedItems = []
for i in itemsLikedByUser:
distances, indices = knn.kneighbors(csr_sample[i], n_neighbors=3)
indices = indices.flatten()
indices = indices[1:]
recommendedItems.extend(indices)
recommendedItems=dataFormatting(recommendedItems)
print("Items to be recommended: ",recommendedItems)
'''
youTubeVideoUrlListPlainText = open("YouTubeData/YouTubeVideoID.txt", encoding="utf8")
youTubeVideoUrlListFile=youTubeVideoUrlListPlainText.readlines()
youTubeVideoUrlListTitle = open("YouTubeData/YouTubeVideoTitle.txt", encoding="utf8")
youTubeVideoUrlTitleFile=youTubeVideoUrlListTitle.readlines()
youTubeVideoUrlListTopic = open("YouTubeData/YouTubeVideoTopic.txt", encoding="utf8")
youTubeVideoUrlTopicFile=youTubeVideoUrlListTopic.readlines()
videoDataSteam=1
videoID=1
print("Videos which are recommended for you:")
for f in recommendedItems:
if(f>60):
continue
#print(youTubeVideoUrlListFile[f], end='')
print('{ "videoID":"'+youTubeVideoUrlListFile[f].strip()+'"'+', "videoTitle":"'+youTubeVideoUrlTitleFile[f].strip()+'" , "videoTopic":"'+youTubeVideoUrlTopicFile[f].strip()+'" },')
'''