Research Information System
International Conference on Engineering Technologies (ICENTE’22), Konya, Turkey, 17 - 19 November 2022, pp.152-158
This study presents a new recommendation system
for the online reservation of tourism customers for hotels with
the features they need, saving customers time. This new system
combined collaborative and content-based filtering approaches
and created a new hybrid recommendation system. Two datasets
containing customer information and hotel features were
analyzed by Recency, Frequency, Monetary (RFM) method in
order to identify customers according to their purchasing nature.
The main idea of the recommendation system is establishing
correlations between users and products and make the decision
to choose the most suitable product or information for a
particular user. For example, there is an issue of data overload,
which is a potential problem for many internet users, due to the
many options available on the internet. Filtering, prioritizing and
beneficially presenting relevant information reduces this
overload. There are following three main ways that
recommendation systems can generate a recommendation list for
a user; content-based, collaborative-based and hybrid
approaches1. This paper describes each category and their
techniques in detail. RFM Analysis is used to identify customer
segments by measuring customers' purchasing habits. It is the
process of labeling customers by determining the Recency,
Frequency and Monetary values of their purchases and ranking
them on a scoring model. Scoring is based on how recently they
bought (Recency), how often they bought (Frequency) and
purchase size (Monetary).