Create content_based_filtering.py

content_based_filtering.py

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+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import linear_kernel
+
+# Example item descriptions
+items = [
+    {'id': 101, 'description': 'Vintage camera from the 1950s'},
+    {'id': 102, 'description': 'Classic vinyl record'},
+    {'id': 103, 'description': 'Retro gaming console'}
+]
+
+# Create TF-IDF matrix
+tfidf = TfidfVectorizer(stop_words='english')
+tfidf_matrix = tfidf.fit_transform([item['description'] for item in items])
+
+# Compute cosine similarity
+cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
+
+# Function to get recommendations
+def get_recommendations(item_id, cosine_sim=cosine_sim):
+    idx = next(index for (index, d) in enumerate(items) if d["id"] == item_id)
+    sim_scores = list(enumerate(cosine_sim[idx]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    sim_scores = sim_scores[1:4]
+    item_indices = [i[0] for i in sim_scores]
+    return [items[i]['id'] for i in item_indices]
+
+# Example usage
+print(get_recommendations(101))