Intelligent tourism recommender systems: A survey

Borrís, Joan; Moreno, Antonio; Valls, Aïda

doi:10.1016/j.eswa.2014.06.007

Cited by 391 publications

(170 citation statements)

References 56 publications

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“…A typical user of a travel recommender system is a tourist who is interested in exploring a city and wants to make a tour around (e.g., Yang and Hwang 2013;Borras et al 2014). Such a tour comprises a scheduled list of attractions (museums, heritage sites, shops, parks or other points of specific interest) as well as the trips needed to travel from one point to the other (e.g., Gretzel et al 2004;Gavalas et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimating a latent-class user model for travel recommender systems

Arentze

Kemperman

Aksenov

2018

Inf Technol Tourism

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In determining the selection of sites to visit on a trip tourists have to trade-off attraction values against routing and time-use characteristics of points of interest (POIs). For recommending optimal personalized travel plans an accurate assessment of how users make these trade-offs is important. In this paper we report the results of a study conducted to estimate a user model for travel recommender systems. The proposed model is part of c-Space-a tour-recommender system for tourists on a city trip which uses the LATUS algorithm to find personalized optimal tours. The model takes into account a multi-attribute utility function of POIs as well as dynamic needs of persons on a trip. A stated choice experiment is designed where the current need is manipulated as a context variable and activity choice alternatives are varied. A random sample of 316 individuals participated in the on-line survey. A latent-class analysis shows that significant differences exist between tourists in terms of how they make the trade-offs between the factors and respond to needs. The estimation results provide the parameters of a multi-class user model that can be used for travel recommender systems.

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Section: Introductionmentioning

confidence: 99%

Estimating a latent-class user model for travel recommender systems

Arentze

Kemperman

Aksenov

2018

Inf Technol Tourism

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“…Hence, a good practice is combining them to overcome the mentioned drawbacks. The most recent approaches follow this trend and propose hybrid recommendation methods, including also contextual information [9]. The particular characteristics of the tourism field offer the possibility to define new mechanisms to learn the user's preferences.…”

Section: Tourism and Recommender Systemsmentioning

confidence: 99%

“…The particular characteristics of the tourism field offer the possibility to define new mechanisms to learn the user's preferences. In particular, the contextual information is a key in the success of any recommender of tourist activities [9]. It has also been argued that a smart recommender should provide a diversified list of recommendations, e.g., even if the system knows that the user is interested in going to the beach, it might not be convenient to show a list with just beaches and not to suggest other kinds of related activities.…”

Section: Tourism and Recommender Systemsmentioning

confidence: 99%

“…The latest research results of the classification methods available in the literature lead to the conclusion that creating a monolithic classifier to cover all the variability inherent to most pattern recognition problems is somewhat unfeasible. Thus, many researchers have focused on providing new solutions for each of the three phases of the MCS: generation, selection and integration [9].…”

Section: Dynamic Selection Of Classifiersmentioning

confidence: 99%

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Dynamic Selection of Recommender Systems: An Application to Tourism

Silva¹,

Arleo²

2017

J Tourism Hospit

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“…Results demonstrate that joining the undertaking setting prompts more exact proposals as compared to group recommender system. The author provided an up-to-date and detailed survey of the recommended field, considering various kinds of interfaces, the range, and diversity of different recommendation system algorithms, the functionalities provided by these systems and their use of Artificial Intelligence methods [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Recommender System for Journal Articles using Opinion Mining and Semantics

Sardar¹,

Ferzund²,

Suryani³

et al. 2017

ijacsa

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Abstract-Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author.

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Intelligent tourism recommender systems: A survey

Cited by 391 publications

References 56 publications

Estimating a latent-class user model for travel recommender systems

Estimating a latent-class user model for travel recommender systems

Dynamic Selection of Recommender Systems: An Application to Tourism

Recommender System for Journal Articles using Opinion Mining and Semantics

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