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.
