From 2002819149c0fb126d2548b826fb71cf920176cb Mon Sep 17 00:00:00 2001
From: srivani vinnakota <164530944+sv410@users.noreply.github.com>
Date: Wed, 9 Oct 2024 19:48:29 +0530
Subject: [PATCH] Create content_based_filtering.py

---
 content_based_filtering.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 content_based_filtering.py

diff --git a/content_based_filtering.py b/content_based_filtering.py
new file mode 100644
index 00000000..c1943d15
--- /dev/null
+++ b/content_based_filtering.py
@@ -0,0 +1,28 @@
+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))