Recommendation system for localized products in vending machines
This paper proposes a framework of localized product recommendation system for automatic vending machines applications. The goal is to offer suitable recommendations of localized products to customers in distinct locations. We develop a hybrid technique that combines a meta-heuristic approach, clustering technique, classification, and statistical method. In the approach, an intelligent system is implemented to analyze product attributes and determine localized products based on the transaction data. To prove the feasibility and effectiveness of proposed approach, we implemented the system in several automatic vending machines owned by an information service company of Taiwan. Nine machines were selected and compared from two locations: living lab by Institute for Information Industry of Taiwan at Song-shan District and business office building at Nei-hu District in Taipei. The real life experiments showed that the profit of vending machine increases after applying our system.
vending machines localized products recommendation system set cover problem genetic algorithm machine learning