Earticle

초록

영어

This paper aims to build predictive models for Airbnb Rating using the Big Data systems, which is distributed parallel computing systems. We use Machine Learning algorithms to build models to predict a rating of the Airbnb listing. The Airbnb ratings can help hosts improve the listing and the hospitality to gain more potential customers. On the other hand, the guests can make a decision based on the ratings that previous guests provided. It is essential to understand customer experience and its role in forming customer rating behavior. The overall ratings provided by customers are reflections of their experiences with a product or service. We use Two-Class Classification models to predict if the listing has a high or low rating based on the features of the listing. We compare the results and the performance of rating prediction models. The comparison is illustrated in terms of the accuracy metrics and computing time.

목차

Abstract
Introduction
Related Work
Method
Experimental Procedure with Spark ML
Conclusion
References

키워드

저자

• Savita Yadav [ Faculty of Information Systems, California State University, Los Angeles ]
• Samyuktha Muralidharan [ Faculty of Information Systems, California State University, Los Angeles ]
• Jongwook Woo [ Faculty of Information Systems, California State University, Los Angeles ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658