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Ontology-Based Travel Recommender System

Ontology-Based Travel Recommender System

Shi Pu (Nottingham Trent University, China) and Isibor Kennedy Ihianle (Nottingham Trent University, UK)
Copyright: © 2021 |Pages: 17
DOI: 10.4018/978-1-7998-6992-4.ch019
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Abstract

Recommender systems are designed to suggest information to users according to their preferences. The items could be movies, books, or various kinds of products. Most of the existing recommender systems are based on a database with limited advantages. However, in this chapter, the authors propose a knowledge-driven travel recommender system to integrate semantic data built using web ontology language (OWL) ontology to allow users to find suitable destinations that fulfil users' travel preferences. This work aims to develop a travel recommendation tool and to examine the reliability, the usability of the system, and satisfaction rate of users. They are also able to demonstrate that users can obtain desired results through queries on the ontology-based system. The overall evaluation of the system shows that users are happy and satisfied with the recommendation results.
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2 Literature Review

Recent related research work provides many different recommendation processes techniques based on customer’s preferences and other information relevant to the user. One of the travel business-related recommender systems was developed by (Artemenko, Pasichnyk, Kunanec, & Tabachyshyn, 2019). This system focused on delivering real-time recommendations of attractions based on users’ location information retrieved from mobile devices/sensors and supported by context analysis. The authors discussed ways of improving recommendations by analyzing the contexts. One of the most important views were on the ways of retrieving high quality, desired and contextual information. According to Ricci, Rokach, & Shapira, (2015) contextual information could be provided by:

  • 1.

    The user directly.

Information and data are provided directly from users’ responses or embedded sensors. For instance, user refusing to grant permission for the sharing of location information, the system could get the location information from the questions that the user needs to answer.

  • 2.

    Implied information.

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