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Home
Python 3
Building the Recommendation Function

Building the Recommendation Function

Building the Recommendation Function

Building the Recommendation Function

The recommendation function is the core component of the project. It accepts a news headline as input, identifies the corresponding article in the dataset, calculates similarity scores, and returns the most similar news articles.

Code

def recommend_news(title):
index = indices[title]
similarity_scores = list(
enumerate(similarity_matrix[index])
)
similarity_scores = sorted(
similarity_scores,
key=lambda x: x[1],
reverse=True
)
return similarity_scores

Explanation

The function performs the following operations:

Finds the index of the selected news article.
Retrieves similarity scores from the similarity matrix.
Sorts the articles based on similarity scores.
Returns the sorted list of similar news articles.

Output

The recommendation function is successfully created and is ready to generate personalized recommendations.

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Building the Recommendation Function

Contents

Building the Recommendation Function

Code

Explanation

Output

News Recommendation System Using Machine Learning

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