Hybrid recommender system for tourism based on big data and AI: A conceptual framework

Abstract

With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been developed. In this article, we present an overview of the various recommendation approaches used in the field of tourism. From this study, an architecture and a conceptual framework for tourism recommender system are proposed, based on a hybrid recommendation approach. The proposed system goes beyond the recommendation of a list of tourist attractions, tailored to tourist preferences. It can be seen as a trip planner that designs a detailed program, including heterogeneous tourism resources, for a specific visit duration. The ultimate goal is to develop a recommender system based on big data technologies, artificial intelligence, and operational research to promote tourism in Morocco, specifically in the Dara â-Tafilalet region.

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

“…In [44], the authors present an architecture and a conceptual framework for a hybrid tourism recommender system based on big data and artificial intelligence. In [45], the authors propose a hybrid recommendation system to combine the predictions from the content-based filtering (CB), collaborative filtering (CF) and demographic filtering (DF) approaches using the neural network model; they compare their results with each of the traditional approaches individually, providing a better focus for recommending tourist sites in Taiwan.…”

A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)

“…RS(s) have etched a spot for the masses through sturdy influence in various areas and have great success utilizing these systems nowadays (Yue, Wang, Zhang, & Liu, 2021). Fararni, K. A. et al (2021) developed a Hybrid Recommender System for Tourism based on big data and AI. The study also aims to gain more visitor experience by recommending relevant items and helping visitors to personalize trips.…”

“…Authors proved that their study solves the overcome information-overload problem triggered in the Tourism portal. Project ATHENA used hybrid filtering, the same with Fararni et al (2021), a type of RS filtering in a combination of CF and CBF, to recommend an item with personalization and receive an email notification recommendation. The technique was based on recent user activity and search but limited in demographic filtering.…”

Implementing Recommender Systems using Machine Learning and Knowledge Discovery Tools

“…The goal of this approach is offering consumers products they have not yet seen it, based on the rating history and tastes of similar users' profiles, the similarity between different consumers as well as products (items, videos, pages, books) that are used in recommended system [32]. Collaborative Filtering cannot apply semantics and systematically analyzes for products.…”