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hotel_recommendation_system.py
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hotel_recommendation_system.py
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#!/usr/bin/env python
# coding: utf-8
# In[12]:
import pandas as pd
pd.set_option('display.max_row',500)
pd.set_option('display.max_columns',100)
pd.set_option('display.unicode.east_asian_width',True)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import TfidfVectorizer
# In[108]:
def getRecommendation(cosine_sim):
simScores = list(enumerate(cosine_sim[-1])) #enumerate()열거해줌
simScores = sorted(simScores, key=lambda x: x[1], reverse=True) #내림차순
simScores = simScores[2:31]
hotel_idx = [i[0] for i in simScores]
RecHotellist = df_review_one_sentence.iloc[hotel_idx]
return RecHotellist.hotel_name
# In[84]:
df_review_one_sentence = pd.read_csv('./hotel/onesentence_hotel_review_final.csv', index_col=0)
#print(df_review_one_sentence.head())
print(df_review_one_sentence.info())
# In[104]:
hotel_idx = df_review_one_sentence[df_review_one_sentence['hotel_name']=='제주 아름다운 리조트'].index[0] #인덱스 알아내는 방법
# In[105]:
Tfidf = TfidfVectorizer()
Tfidf_matrix = Tfidf.fit_transform(df_review_one_sentence['review_one_sentence'])
#print(Tfidf_matrix.shape)
#print(Tfidf_matrix)
# In[106]:
cosine_sim = linear_kernel(Tfidf_matrix[hotel_idx], Tfidf_matrix) #호텔1개에 대한 588개호텔의 수치
#print(cosine_sim.shape)
# In[109]:
print(getRecommendation(cosine_sim))
# In[ ]: