TY - JOUR
T1 - A hybrid machine learning approach to hotel sales rank prediction
AU - Srivastava, Praveen Ranjan
AU - Eachempati, Prajwal
AU - Charles, Vincent
AU - Rana, Nripendra P.
N1 - Publisher Copyright:
© Operational Research Society 2022.
PY - 2023
Y1 - 2023
N2 - One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
AB - One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
KW - ANN
KW - Sentiment analysis
KW - predictive model
KW - regression analysis
KW - sales rank prediction
UR - http://www.scopus.com/inward/record.url?scp=85134575387&partnerID=8YFLogxK
U2 - 10.1080/01605682.2022.2096498
DO - 10.1080/01605682.2022.2096498
M3 - Article
AN - SCOPUS:85134575387
SN - 0160-5682
VL - 74
SP - 1407
EP - 1423
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
IS - 6
ER -