Recommender Systems in Tourism

Ricci, Francesco

doi:10.1007/978-3-030-05324-6_26-1

Cited by 18 publications

(12 citation statements)

References 34 publications

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“…Em meados da década de 1990, aárea de SR passou a ser reconhecida como umá area de pesquisa independente e desde então estudos específicos e correlatos cresceram consideravelmente, motivados principalmente por dois fatores: o primeiro está relacionado com os casos de sucessos dos sites de empresas como Amazon, Netflix, Youtube, Yahoo, Tripadvisor, entre outros, que passaram a usar SR nos seus respectivos negócios; e o segundo tem a ver com o incentivo por parte de associações de pesquisa, como a ACM 1 que passou a organizar congressos e workshops específicos em SR (RICCI et al, 2011).…”

Section: Sistemas De Recomendaçãounclassified

“…SR são ferramentas capazes de fazer sugestões de "itens" de diversasáreas dentro de um processo de tomada de decisão por parte dos usuários (RICCI et al, 2011). Usuários leigos ou especializados quando aceitam sugestões fornecidas pelos SR, se beneficiam das recomendações, principalmente das que foram formuladas especificamente para eles e, ao mesmo tempo, colaboram com o aprendizado do sistema, que registra as interações deles afim de melhorar as recomendações futuras, seja para o mesmo usuário ou para outros.…”

Section: Sistemas De Recomendaçãounclassified

“…Sistemas de Recomendação (SR) são softwares nos quais implementa-se uma ou mais técnicas que são capazes de sugerir itens para os usuários (RICCI et al, 2011). Mais especificamente, SR são sistemas capazes de prover recomendações: com base no histórico de interações entre os usuários e o sistema; entre os usuários e os itens presentes no sistema;…”

Section: Introductionunclassified

“…A recomendação baseada em conteúdo considera dados não estruturados a respeito do item sob recomendação, por exemplo o texto completo (conteúdo) de uma notícia. Finalmente, os SR baseados em conhecimento criam as recomendações com base em informações de domínio e de preferências dos usuários (RICCI et al, 2011).…”

Section: Introductionunclassified

“…Desde sua criação, aárea de SR teve uma rápida expansão devidoà evolução de aplicações que recomendam filmes, páginas web, notícias, tratamentos médicos, músicas e produtos em geral (RICCI et al, 2011). No que se refereà recomendação de notícias, Kunaver e Požrl (2016) define que as pessoas leem notícias para saber o que aconteceu e o que acontecerá em uma cidade, região, país ou no mundo.…”

Section: Introductionunclassified

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Uma abordagem híbrida para sistemas de recomendação de notícias

Pagnossim¹

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Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user's expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user's interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a "case" model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS ; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users.

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Section: Sistemas De Recomendaçãounclassified