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Python 3
Building the KNN Similarity Model

Building the KNN Similarity Model

Building the KNN Similarity Model

Building the KNN Similarity Model

With numerical genre vectors prepared, we now build the similarity model using the K-Nearest Neighbors algorithm. This model identifies movies that are most similar to a given movie based on genre composition.

Code:

from sklearn.neighbors import NearestNeighbors

nn = NearestNeighbors(metric='cosine', algorithm='brute')

nn.fit(tfidf_matrix)

Cosine similarity measures how closely two movies align in terms of genres. Using KNN avoids computing a full similarity matrix, making the approach more memory-efficient and suitable for larger datasets.

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Building the KNN Similarity Model

Contents

Building the KNN Similarity Model

Movie Recommendation System Project Using Content-Based Filtering

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