A PERSONALIZED AND LOCATION-BASED RECOMMENDATION SYSTEM FOR FLORISTS USING GEOCODING AND COLLABORATIVE FILTERING

Abstract

The florist industry is growing rapidly and has become increasingly competitive, making it challenging for customers to choose the right florist for their needs. Customers may have a difficult time choosing the right florist for their needs, as they may be overwhelmed by the number of options available or unsure about the quality and reliability of a particular florist. Furthermore, customers may not have a clear idea of what they are looking for in a florist or may have specific preferences that are difficult to articulate. This can make it challenging for businesses to effectively market their services and connect with potential customers. A recommendation system that leverages collaborative filtering and geocoding can help solve these problems by providing customers with personalized recommendations based on their preferences and location. This can help customers make more informed decisions and simplify the process of selecting a florist. Therefore, to solve this problem, a personalized and location-based recommendation system for florists that utilizes collaborative filtering and geocoding is needed to recommends the right florists to customers based on their preferences and location. The proposed system uses collaborative filtering to analyze customer preferences and generate personalized recommendations. It also utilizes geocoding to determine the location of the customer and the florists, allowing the system to recommend nearby florists. Haversine formula will be used to calculate the distance between the customer and florists. The recommendation system was implemented as a web-based application to enable easy access for users.