Design and Implementation of a Personalized Tourism Recommendation System Based on the Data Mining and Collaborative Filtering Algorithm

Abstract

A personalized tourism recommendation system provides convenient and economically affordable travel information for individuals/groups. This recommendation system banks on accumulated and analyzed data for providing context-aware travel solutions. For improving the recommendation efficiency and data analysis of such systems, this article introduces a mining and filtering harmonized collaborative process, named as the collaborative mining and filtering process (CMFP), for reducing the data processing overheads and improving the recommendation ratio. In this process, the accumulated data from the global and personal travel, expenditure, and other information are collaboratively analyzed. This analysis is powered by knowledge-based transfer learning for reducing the retardation in the large data processing. Based on the context-based data analysis, the filtering and mining are jointly performed for providing recommendations. In the filtering process, the maximum processed contextual data are extracted for updating the current knowledge base. From this base, the recommendation for adaptable travel is recommended for the user. This process’s performance is analyzed using the metrics accuracy, data handling rate, mining time, and overhead.

Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations

“…CF is one of the most commonly used techniques in tourism RSs [11]. The CF method recommends tourist destinations according to their relevance to tourists' preferences [12].…”

“…Machine learning is widely used in various fields, such as tourism RSs, to enhance their efficiency [11]. For instance, Jomsri [8] applied machine learning and analytic hierarchy process techniques to develop a system that guides boat travel destinations in Om Non-Canal, Thailand.…”

“…Another research paper conducted by Nan et al [11] introduced a collaborative mining and filtering process for context-aware travel recommendations. The performance of their approach was compared to other methods, which include methods-time measurement, average true range, and context-aware fuzzy-ontology-based tourism recommendation system.…”

Real-Time Context-Aware Recommendation System for Tourism

“…Tourism service tools include a CF-based RS method that recommends tourist attractions according to their relevance to tourists and a CB-based RS method that recommends tourist attractions according to their relevance to tourist destinations [ 11 ]. The CF-based RS assumes that similar tourists have similar preferences for a specific tourist destination based on interaction data between tourists and tourist destinations [ 12 ]. Because CF is performed based on interactions between tourists and tourist destinations, recommendations can be made even if the similarity between tourist destinations is not high.…”

ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization

“…Deep learning-based recommendation systems are gaining popularity in the recommendation task [21]. These systems use neural networks to learn complex patterns in user behaviour and item attributes to make personalized recommendations [22].…”