Create content_based_filtering.py (Anjaliavv51#387)

Description
<!–Please include a brief description of the changes–> Added data
collection and recommendation model features. The data collection tracks
user interactions (clicks, views, purchases) and stores them in a
database. The recommendation model uses collaborative filtering and
content-based filtering to provide personalized recommendations.

Related Issues
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simply state “None”–>

Closes Anjaliavv51#123 (Replace with the actual issue number if applicable)
Type of PR
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the below box -->

[ ] Feature
Screenshots / videos (if applicable)
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N/A

Checklist
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[X] I have gone through the contributing guide
[X] I have updated my branch and synced it with project main branch
before making this PR
[X] I have performed a self-review of my code
[X] I have tested the changes thoroughly before submitting this pull
request.
[X] I have provided relevant issue numbers, screenshots, and videos
after making the changes.
[X] I have commented my code, particularly in hard-to-understand areas.
Additional context:
<!–Include any additional information or context that might be helpful
for reviewers.–> This feature enhances user experience by providing
personalized recommendations and insights into user behavior, which can
inform marketing strategies and inventory management.

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))