The Tourist Attractions Recommender System for Bangkok Thailand

Abstract

The objective of this research is to design and develop a tool to evaluate tourists' satisfaction with the attractions recommendation system in Bangkok, Thailand. We have four main stages for the tourist attraction recommendation system. The first stage is to fill imputed missing values with association rules and multiple imputations. The second stage is constructing the tourist attractions recommendation model by ranking the tourist attractions with a ranking method and similarity measurements based on a personal recommender system with cosine algorithm. The third stage is to design and develop the personal recommender website. And the last stage is to evaluate the personal recommender system with four measurements: accuracy, precision, f-measure, and g-mean. The experiment results from a sampling of thirty people found that the tourist attraction recommendation system can: 1) make a positive recommendation 340 times, but 105 times will not meet the needs, and 2) make a negative recommendation 708 times, but 77 times will meet the needs. The results show that the tourist attractions recommendation system has satisfactory performance and reliability with high accuracy, precision, and f-measure, and g-mean values of 85.20%, 76.40%, 78.89%, and 84.26%, respectively. In addition, it was found that the users' satisfaction towards the system was at a high level with a value of 4.60. This means that the proposed tourist attractions recommendation system can be used to recommend personal preferences as well.

5G-Based Information Platform Design of Tourist Attractions in the Context of Mobile Big Data

“…rough simulation experiments, number theory transformation (NTT) verified that the use of NOMA technology in urban areas can increase the total throughput of wireless access macrocells by about 50% [18]. NOMA technology can increase the spectral efficiency of weak-signal mobile users by about 48%, and the spectral efficiency of strong-signal mobile users by about 32% [19].…”