Hotel recommendation system with machine learning
The hospitality industry has witnessed a notable increase in the utilisation of online booking platforms and the reliance on online reviews in recent years. This phenomenon presents a challenge for customers in their search for a suitable hotel that aligns with their specific requirements. Mac...
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Format: | Final Year Project / Dissertation / Thesis |
Published: |
2023
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Subjects: | |
Online Access: | http://eprints.utar.edu.my/5997/1/fyp_IA_2023_PCC.pdf http://eprints.utar.edu.my/5997/ |
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Summary: | The hospitality industry has witnessed a notable increase in the utilisation of online booking
platforms and the reliance on online reviews in recent years. This phenomenon presents a
challenge for customers in their search for a suitable hotel that aligns with their specific
requirements. Machine learning techniques were employed to develop a hotel recommendation
system as a means of addressing this issue. The objective of this report is to elucidate the
development process and evaluate the efficacy of the aforementioned system. The CRISP-DM
approach was employed in conducting the present investigation. The system underwent
training using a dataset scraped from Web using beautifulsoup webscraping , consisting of
hotel data that had been preprocessed and transformed into a format suitable for utilisation by
machine learning models. The system employs a database including hotel attributes and
customer evaluations in order to compute the cosine similarity between the characteristics of
each hotel and the user's preferences. The TF-IDF technique is employed to assign weight to
each word in a review, taking into account its frequency across the entire database. By
integrating these two methodologies, the system is capable of delivering tailored
recommendations to users, taking into account their individual tastes. The study's findings
indicate that the implementation of a machine learning-based hotel recommendation system
has the potential to provide customers with valuable suggestions, hence enhancing their hotel
booking experience. This study holds significance as it contributes to the field of hospitality by
providing a practical resolution to the issue of hotel suggestion and addressing the existing
knowledge gap about the utilisation of machine learning for enhancing hotel recommendations. |
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