Store Recommendation and Route Planning System for Improving Shopping Experience of Users

Abstract

Abstract Grocery shopping often presents a frustrating experience for customers due to factors such as price discrepancies, inventory inaccuracies, and the lack of personalized assistance. Existing methods, such as store subscriptions and advertisements, are limited in their ability to deliver tailored, timely information. Furthermore, current applications require users to manually browse multiple marketplaces to locate desired products, lacking automated recommendations based on shopping lists. Addressing this gap, this undergraduate project proposes a store recommendation system designed to enhance the shopping experience. The system targets four main objectives: (1) recommending the most cost-effective stores with efficient routes for purchasing a full shopping list, (2) identifying purchase patterns and predicting future shopping needs, (3) optimizing travel paths considering dynamic conditions, and (4) proposing standardized methods for store data collection. To achieve these goals, several algorithms were implemented: greedy heuristics, branch and bound, beam search, and exact optimization methods for store selection; an adaptive genetic algorithm combined with A* search for route planning; and a Singular Value Decomposition (SVD)-based model for personalized item recommendations. Experimental results demonstrated that optimized store suggestion algorithms delivered near-optimal results within acceptable time constraints, and that route planning methods effectively reduced travel time. Challenges related to real-time inventory data acquisition are also discussed, alongside proposals for future refinements to improve data accuracy and recommendation quality. This work highlights the potential of integrated recommendation and optimization systems to significantly streamline the grocery shopping experience and sets a foundation for future real-world deployments