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# Source:
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Generate DF
df = \
pd.DataFrame({'jobId' : [1, 2, 3, 4, 5],
'serviceId' : [99, 88, 77, 66, 55],
'text' : ['Ich hätte gerne ein Bild an meiner Wand.',
'Ich will ein Bild auf meinem Auto.',
'Ich brauche ein Bild auf meinem Auto.',
'Ich brauche einen Rasenmäher für meinen Garten.',
'Ich brauche einen Maler, der mein Haus streicht.'
# Show DF
# Vectorizer to convert a collection of raw documents to a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
# Learn vocabulary and idf, return term-document matrix.
tfidf = vectorizer.fit_transform(df['text'].values.astype('U'))
# Array mapping from feature integer indices to feature name
words = vectorizer.get_feature_names()
# Compute cosine similarity between samples in X and Y.
similarity_matrix = cosine_similarity(tfidf, tfidf)
# Matrix product
# Instead of using fit_transform, you need to first fit
# the new document to the TFIDF matrix corpus like this:
queryTFIDF = TfidfVectorizer().fit(words)
# We can check that using a new document text
query = 'Mähen Sie das Gras in meinem Garten, pflanzen Sie Blumen in meinem Garten.'
# Now we can 'transform' this vector into that matrix shape by using the transform function:
queryTFIDF = queryTFIDF.transform([query])
# As we transformed our query in a tfidf object
# we can calculate the cosine similarity in comparison with
# our pevious corpora
cosine_similarities = cosine_similarity(queryTFIDF, tfidf).flatten()
# Get most similar jobs based on next text
related_product_indices = cosine_similarities.argsort()[:-11:-1]
# array([3, 2, 1, 4, 0])
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