Problem 1)
Refer to Lab 1 (Problem 2), you have to incorporate business intelligence in your PVFC order processing system by adding a recommendation system.
Recommendation System: A recommendation system for an order processing system analyzes historical order data, user behavior, and item characteristics to predict and suggest products that a customer is likely to purchase next.
These systems:
Recommendation Techniques
Collaborative Filtering (CF): Recommends items based on similar user behavior. If user A and B have similar order histories, the system suggests items purchased by B to user A.
Content-Based Filtering: Suggests items similar to those a user has already purchased or viewed, based on item attributes (e.g., category, brand, or price).
Hybrid Systems: Combines collaborative and content-based filtering to overcome limitations like the "cold start" problem (new users/items with no history).
Association Rule Mining: Identifies items frequently bought together, ideal for "frequently bought together" bundles or upselling complementary items.
Assumption: Sufficient numbers of orders are present in database.
Functionalities to be added
(i) Display recommendation messages during Product Selection and Order Placement: "Customers who bought this also bought..."
(ii) Reordering suggestions for customers
(iii) For employees/managers of PVFC, forecasting demand for secondary items when a primary item's sales increase (Inventory Management)