A novel tourism recommender system in the context of social commerce (original) (raw)
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Recommender systems (RSs) are information search and filtering tools that provide suggestions for items to be of use to a user. They are now common in many Internet applications (Google News, Amazon, TripAdvisor), helping users to make better choices while searching for news, books, or vacations. RSs exploit data mining and information retrieval techniques to predict to what extent an item fits the user needs and wants. RSs interact with the user to finetune these suggestions while presenting a selection of the items, among those having the largest predicted fit score. RSs have been used in tourism applications for suggesting points of interest to visit, holiday properties, and flights, or even generating complete plans for holidays, that is, bundling different types of more
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The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation...
A Review on Content Based Recommender Systems in Tourism
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Recommender systems (RSs) have been used worldwide in several fields to facilitate the tourists’ planning activities. Tourism is one of the fields that uses RSs to reduce the overload of information to end users. Accordingly, tourists get recommendations that are most suitable to their profiles. This paper presents a detailed overview of the tourism recommender systems that were developed since 2008. It focuses mainly on the content based systems and their applications in the tourism field.
Trends and Techniques used in Tourist Recommender System : A Review
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Traveling to other locations for pleasure, business, or other reasons is called tourism. In every sort of recommender system, there are a certain amount of users and items. Creating a recommendation systems is made more difficult by the abundance of information available online and the high volume of website visits. A recommender system pulls the user's preferences or interests from relevant data sets to reduce information overload. This calls for the development of a new recommended system that will deliver higher-quality recommendations for massive data sets. To solve these kinds of problems, we have found several approaches for making recommendations, including three different types: content-based filtering, collaborative filtering, and hybrid filtering. With each type of recommender system, this research also analyses several algorithms. The main aim of this paper is to review several trends and techniques currently being used in tourist recommender systems.
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Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years. (J. Borràs), antonio.moreno@urv. cat (A. Moreno), aida.valls@urv.cat (A. Valls).