A Hotel Recommender System for Tourists Using the Artificial Bee Colony Algorithm and Fuzzy TOPSIS Model: A Case Study of TripAdvisor

Recommendation systems play an indispensable role in tourists' decision-making process. An important issue for tourists concerns the selection of accommodation in accordance with the criteria on their minds, which may include several items at the same time. This paper proposes a novel approach to recommendation systems in the tourism industry involving a combination of the Artificial Bee Colony (ABC) algorithm and the fuzzy TOPSIS model. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making method, has been utilized to optimize the system. The solution presented in this research includes two major parts, where the employed ABC algorithm has been improved and is more efficient than the standard version. This research has addressed the TripAdvisor dataset and presented a method for hotel recommendations based on user preferences according to real data. The obtained results demonstrate the high accuracy of the method presented in the research.

Automated summary

This paper proposes a novel approach to recommendation systems in the tourism industry, involving a combination of the ABC algorithm and the fuzzy TOPSIS model. The research presents a method for hotel recommendations based on user preferences according to real data, demonstrating high accuracy in the obtained results.