114 lines
3.4 KiB
Python
114 lines
3.4 KiB
Python
# pip install mysql.connector
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import mysql.connector
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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import logging
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def database():
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db_connection = mysql.connector.connect(
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host = "localhost",
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port = "3306",
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user = "root",
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database = "Marketplace"
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)
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return db_connection
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def get_all_products():
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db_con = database()
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cursor = db_con.cursor()
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cursor.execute("SELECT CategoryID FROM Category")
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categories = cursor.fetchall()
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select_clause = "SELECT p.ProductID"
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for category in categories:
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category_id = category[0]
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select_clause += f", MAX(CASE WHEN pc.CategoryID = {category_id} THEN 1 ELSE 0 END) AS `Cat_{category_id}`"
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final_query = f"""
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{select_clause}
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FROM Product p
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LEFT JOIN Product_Category pc ON p.ProductID = pc.ProductID
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LEFT JOIN Category c ON pc.CategoryID = c.CategoryID
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GROUP BY p.ProductID;
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"""
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cursor.execute(final_query)
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results = cursor.fetchall()
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final = []
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for row in results:
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text_list = list(row)
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text_list.pop(0)
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final.append(text_list)
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cursor.close()
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db_con.close()
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return final
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def get_user_history(user_id):
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db_con = database()
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cursor = db_con.cursor()
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cursor.execute("SELECT CategoryID FROM Category")
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categories = cursor.fetchall()
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select_clause = "SELECT p.ProductID"
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for category in categories:
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category_id = category[0] # get the uid of the catefory and then append that to the new column
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select_clause += f", MAX(CASE WHEN pc.CategoryID = {category_id} THEN 1 ELSE 0 END) AS `Cat_{category_id}`"
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final_query = f"""
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{select_clause}
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FROM Product p
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LEFT JOIN Product_Category pc ON p.ProductID = pc.ProductID
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LEFT JOIN Category c ON pc.CategoryID = c.CategoryID
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where p.ProductID in (select ProductID from History where UserID = {user_id})
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GROUP BY p.ProductID;
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"""
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cursor.execute(final_query)
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results = cursor.fetchall()
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final = []
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for row in results:
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text_list = list(row)
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text_list.pop(0)
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final.append(text_list)
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cursor.close()
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db_con.close()
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return final
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def get_recommendations(user_id, top_n=40):
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try:
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# Get all products and user history with their category vectors
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all_products = get_all_products()
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user_history = get_user_history(user_id)
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# if not user_history:
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# # Cold start: return popular products
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# return get_popular_products(top_n)
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# Calculate similarity between all products and user history
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user_profile = np.mean(user_history, axis=0) # Average user preferences
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similarities = cosine_similarity([user_profile], all_products)
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# finds the indices of the top N products that have the highest
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# cosine similarity with the user's profile and sorted from most similar to least similar.
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product_indices = similarities[0].argsort()[-top_n:][::-1]
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print("product", product_indices)
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# Return recommended product IDs
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return [all_products[i][0] for i in product_indices] # Product IDs
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except Exception as e:
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logging.error(f"Recommendation error for user {user_id}: {str(e)}")
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# return get_popular_products(top_n) # Fallback to popular products
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get_recommendations(1)
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