The main objective of this research was to propose a technological solution to the long queues that are often seen in many retail outlets. As the solution this research proposes a self-checkout application. The application populates a list predicted next purchasing item set making the user interface intelligent and user friendly. The research introduces a model named RFR-U model to generate the next purchasing item list of the customer. It uses the parameters; relevance, recency and frequency to determine the next purchasing item set. The algorithm uses a rule based approach with weighted ratings. Although collaborative method is a popular method in finding such results, in the studied scenario, it is not applicable as the store does not maintain a comprehensive user profiles or facilitates the users to rate products. The proposed algorithm and the solution was evaluated both quantitatively and qualitatively and results show an accuracy above 80%.
Keywords: Frequency, Recency, Recommendation systems, Relevance, purchasing patterns, self-checkout
