CURUMIM: A Serendipitous Recommender System for Tourism Based on Human Curiosity

Menk, Alan; Sebastiá, Laura; Ferreira, Rebeca

doi:10.1109/ictai.2017.00124

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…A good number of those properties could be based on the personality predicted using data available on SNs, following some already existent psychological theories. For instance, the system could recommend useful POIs in a reduced quantity when considering the curiosity, that means, the higher the degree of curiosity, the lower the popularity of the POI, and vice-versa (Menk et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Recommendation Systems for Tourism Based on Social Networks: A Survey

Menk¹,

Sebastiá²,

Ferreira³

2019

Preprint

Self Cite

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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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Section: Discussionmentioning

confidence: 99%

Recommendation Systems for Tourism Based on Social Networks: A Survey

Menk¹,

Sebastiá²,

Ferreira³

2019

Preprint

Self Cite

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“…Recently, some studies have investigated the benefits of serendipity in the recommendation systems also in the tourism domain. In [30], Menk et al aimed to surprise the users with serendipitous recommendations of places exploiting their degree of curiosity and education. In their study, the authors extracted information from social networks (e.g.…”

Section: Recommendation In Tourismmentioning

confidence: 99%

“…This definition was made accordingly with the conceptualization of the hyper-local concept as related to a small community or geographical area (Source: https://www. oxfordlearnersdictionaries.com/definition/english/hyperlocal) much such possibility can benefit in terms of serendipity [30] and diversifying experiences [13]. Each dimension has been investigated in our evaluation through the implementation of three recommendation criteria, to exploit possible advantages and/or limitations in fostering interactions between locals and visitors in ShareCities.…”

Section: Introductionmentioning

confidence: 99%

Exploring proximity-based recommendation criteria as a tool for information exchange and interactions between locals and tourists

Ceccarini

Nisi

Prandi

2022

Multimed Tools Appl

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Sharing economy and contemporary tourism are two emerging concepts that urge to be investigated together with new ubiquitous and immersive technologies, in the tourism and hospitality sector. In this rich scenario, we designed and implemented ShareCities, a platform to foster remote direct information exchange and meaningful interactions among tourists and locals. Exploiting ShareCities we here present an extended analysis on the opportunity to use people-to-people recommendation criteria based on proximity. We hence defined three criteria which drove our analysis: i) profile similarity, ii) geographical proximity, and iii) random exploration. Through an online questionnaire, we collect answers from 126 young-adult students, obtaining a general positive interest in the three criteria but also concerns in terms of privacy, trust, and feeling of disorientation.

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“…To automatically deduce users' preferences from their social networks, many techniques are used, such as opinion mining (Logesh et al, 2018;Logesh and Subramaniyaswamy, 2019), analysis of implicit/explicit feedback (Hidasi and Tikk, 2016), and analyzing geotagged pictures (Sun et al, 2019). Curumim (Menk et al, 2017) takes from users' social networks their travel history and level of education, and predicts their degree of curiosity. Most of these works follow the PULL approach.…”

Section: User Informationmentioning

confidence: 99%

“…Authors of (Shen et al, 2016) assert that the proposed system is able to recommend fresh and surprise POI, based on collective intelligence. From the level of curiosity predicted from users' social networks, Curumim (Menk et al, 2017) adapts the degree of surprise and unexpectedness of a recommended POI, tailored to users' curiosity values.…”

Section: Serendipitymentioning

confidence: 99%

Context-aware and Ontology-based Recommender System for e-Tourism

Castellanos

Cardinale

Roose

2021

Proceedings of the 16th International Conference on Software Technologies

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Frequently, travelers try to collect information for planing a trip or when being at the destination. Usually, tourists depend on places' reviews to make the choice, but this implies prior knowledge of the touristic places and explicit search for suggestions through interaction with applications (i.e., PULL paradigm). In contrast, a PUSH approach, in which the application proactively triggers a recommendation process according to users' preferences and when necessary, -seems to be a more reasonable solution. Recommender systems have become appropriate applications to help tourists in their trip planning. However, they still have limitations, such as poor consideration of users' profiles and their contexts, their predictable suggestions, and the lack of a standard representation of the knowledge managed. We propose a user-centric recommender system architecture, that supports both PULL and PUSH approaches, assisted by an ontology-based spreading activation algorithm for context-aware recommendations, with a focus on decreasing predictable outputs and increasing serendipity, based on an aging-like approach. To demonstrate its suitability and performance, we develop a first prototype of the architecture and simulate different scenarios, varying users' profiles, preferences, and context parameters. Results show that the ontology-based spreading activation and the proposed aging system provide relevant and varied recommendations according to users' preferences, while considering their context and improving the serendipity of the system when comparing with a state-of-the-art work.

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CURUMIM: A Serendipitous Recommender System for Tourism Based on Human Curiosity

Cited by 18 publications

References 17 publications

Recommendation Systems for Tourism Based on Social Networks: A Survey

Recommendation Systems for Tourism Based on Social Networks: A Survey

Exploring proximity-based recommendation criteria as a tool for information exchange and interactions between locals and tourists

Context-aware and Ontology-based Recommender System for e-Tourism

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