tourism recommender systems survey

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Tourism recommendation system: a survey and future research directions

• 1209: Recent Advances on Social Media Analytics and Multimedia Systems: Issues and Challenges
• Published: 21 April 2022
• Volume 82 , pages 8983–9027, ( 2023 )

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• Joy Lal Sarkar 1 ,
• Abhishek Majumder 1 ,
• Chhabi Rani Panigrahi 2 ,
• Sudipta Roy 3 &
• Bibudhendu Pati 2  

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A Recommendation System (RS) is an intelligent computer based system which provide valuable suggestions to the user and are used in several domains. Social media platforms are the most common internet applications due to the large number of users. The numerous posts, likes, etc. have accrued on social media platforms and can be used in variety of recommendation systems. In this work, the primary focus is the tourism domain, where RS serves as a valuable tool for the tourist to plan his trip. Traditional RS systems only cater to the needs of the tourist by examining few factors. However, there are a large range of factors such as environment factors , actual geo-coordinates, trip destination, preferences of the user etc. that need to be taken into account in order to make a foolproof recommendation to the tourists. Tourism Recommendation Systems (TRS) provide suggestions to the tourists to identify the most suited transport (flight, train, etc.), accommodations, museums, special interest places and other items which are required for the trip. Several techniques are used and a thorough study of various techniques of traditional RS and TRS techniques have been done which are specially designed for tourism domain. Various Artificial Intelligence (AI) techniques have been highlighted which are used to solve the tourist recommendation problem. Also, future research direction has been suggested which would improvise the Quality of Service (QoS) of the RS in tourism industry.

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Tripura University, Tripura, India

Joy Lal Sarkar & Abhishek Majumder

Rama Devi Women’s University, Bhubaneswar, India

Chhabi Rani Panigrahi & Bibudhendu Pati

Assam University, Silchar, India

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Sarkar, J.L., Majumder, A., Panigrahi, C.R. et al. Tourism recommendation system: a survey and future research directions. Multimed Tools Appl 82 , 8983–9027 (2023). https://doi.org/10.1007/s11042-022-12167-w

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DOI : https://doi.org/10.1007/s11042-022-12167-w

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Title: recommendation systems for tourism based on social networks: a survey.

Abstract: Nowadays, recommender systems are present in many daily activities such as online shopping, browsing social networks, etc. Given the rising demand for reinvigoration of the tourist industry through information technology, recommenders have been included into tourism websites such as Expedia, Booking or Tripadvisor, among others. Furthermore, the amount of scientific papers related to recommender systems for tourism is on solid and continuous growth since 2004. Much of this growth is due to social networks that, besides to offer researchers the possibility of using a great mass of available and constantly updated data, they also enable the recommendation systems to become more personalised, effective and natural. This paper reviews and analyses many research publications focusing on tourism recommender systems that use social networks in their projects. We detail their main characteristics, like which social networks are exploited, which data is extracted, the applied recommendation techniques, the methods of evaluation, etc. Through a comprehensive literature review, we aim to collaborate with the future recommender systems, by giving some clear classifications and descriptions of the current tourism recommender systems.

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• da Costa C Nascimento M (2024) The priority-based traveling backpacker problem Expert Systems with Applications: An International Journal 10.1016/j.eswa.2023.121818 238 :PA Online publication date: 15-Mar-2024 https://dl.acm.org/doi/10.1016/j.eswa.2023.121818
• Goldstein A Hajaj C (2024) Measuring flight-destination similarity Expert Systems with Applications: An International Journal 10.1016/j.eswa.2023.121802 238 :PA Online publication date: 15-Mar-2024 https://dl.acm.org/doi/10.1016/j.eswa.2023.121802
• Adamo T Colizzi L Dimauro G Ghiani G Guerriero E (2024) A multi-modal tourist trip planner integrating road and pedestrian networks Expert Systems with Applications: An International Journal 10.1016/j.eswa.2023.121457 237 :PB Online publication date: 1-Feb-2024 https://dl.acm.org/doi/10.1016/j.eswa.2023.121457
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• Cena F Console L Micheli M Vernero F (2023) Combining Genetic Algorithms and Temporal Constraint Satisfaction for Recommending Personalized Tourist Itineraries AIxIA 2023 – Advances in Artificial Intelligence 10.1007/978-3-031-47546-7_30 (441-452) Online publication date: 6-Nov-2023 https://dl.acm.org/doi/10.1007/978-3-031-47546-7_30
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A survey on mobile tourism Recommender Systems

2013, 2013 Third International Conference on Communications and Information Technology (ICCIT)

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Damianos Gavalas

Recommender Systems (RS) have been extensively utilized as a means of reducing the information overload and offering travel recommendations to tourists. The emerging mobile RSs are tailored to mobile device users and promise to substantially enrich tourist experiences, recommending rich multimedia content, context-aware services, views/ratings of peer users, etc. New developments in mobile computing, wireless networking, web technologies and social networking leverage massive opportunities to provide highly accurate and effective tourist recommendations that respect personal preferences and capture usage, personal, social and environmental contextual parameters. This article follows a systematic approach in reviewing the state-of-the-art in the field, proposing a classification of mobile tourism RSs and providing insights on their offered services. It also highlights challenges and promising research directions with respect to mobile RSs employed in tourism.

Francesco Ricci

Abstract: Mobile phones are becoming a primary platform for information access and when coupled with recommender system technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems, providing personalized and more focused content, hence limiting the negative effects of information overload.

Abstract In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings.

Expert Systems with Applications

ABSTRACT Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device.

Mobile tourist guides have attracted considerable research interest during the past decade resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalisation of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of Wireless Sensor Network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about Points of Interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.

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