Create calculate_cosine_similarity.py

calculate_cosine_similarity.py

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+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+def calculate_cosine_similarity(user_embedding, product_embeddings, product_ids, top_n=5):
+    user_embedding = user_embedding.reshape(1, -1)
+    product_embeddings = np.array(product_embeddings)
+
+    similarities = cosine_similarity(user_embedding, product_embeddings).flatten()
+    top_indices = similarities.argsort()[::-1][:top_n]
+    recommendations = [(product_ids[i], similarities[i]) for i in top_indices]
+
+    return recommendations