An interactive interface for instilling trust and providing diverse recommendations

Katarya, Rahul; Jain, Ivy; Hasija, Hitesh

doi:10.1109/iccct.2014.7001463

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…In this work, we employ the treemap visualization in its traditional form because we believe these two properties to be important for depicting the search space as completely as possible. Furthermore, the focus of Katarya et al [29] differs from ours. They concentrate on different ways to visualize explanations for recommendations, whereas explanations are rather an add-on in our proposal.…”

Section: Related Work and Research Objectivescontrasting

confidence: 46%

“…Based on our literature search, we can verify this claim and confirm that it still holds. The only proposal going into this direction is the treemap-like component presented by Katarya et al [29], which is included in their interface containing several visual explanation styles for movie recommendations. We call it "treemap-like" because we are missing two distinctive properties of treemaps (cf.…”

Section: Related Work and Research Objectivesmentioning

confidence: 99%

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Explorative Analysis of Recommendations Through Interactive Visualization

Richthammer

Pernul

2017

Lecture Notes in Business Information Processing

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Abstract. Even though today's recommender algorithms are highly sophisticated, they can hardly take into account the users' situational needs. An obvious way to address this is to initially inquire the users' momentary preferences, but the users' inability to accurately state them upfront may lead to the loss of several good alternatives. Hence, this paper suggests to generate the recommendations without such additional input data from the users and let them interactively explore the recommended items on their own. To support this explorative analysis, a novel visualization tool based on treemaps is developed. The analysis of the prototype demonstrates that the interactive treemap visualization facilitates the users' comprehension of the big picture of available alternatives and the reasoning behind the recommendations. This helps the users get clear about their situational needs, inspect the most relevant recommendations in detail, and finally arrive at informed decisions.

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Section: Related Work and Research Objectivescontrasting

confidence: 46%

Section: Related Work and Research Objectivesmentioning

confidence: 99%

Explorative Analysis of Recommendations Through Interactive Visualization

Richthammer

Pernul

2017

Lecture Notes in Business Information Processing

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“…Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101,143,252,149,85,208,77,189,220,68,64,6,52,29,167,230,23,96,47,242,117,254,15,46,9,156,44,236,88,…”

Section: Selection Of Primary Studiesmentioning

confidence: 99%

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

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“…A proposta de Katarya et al (2014)é uma interface interativa para recomendar filmes. A visualização apresenta o folder do filme e o tamanhoé definido pela avaliação colaborativa, quanto maior, mais interessante ao usuário.…”

Section: Visualização Para Sistemas De Recomendação De Filmesunclassified

Quais as Melhores Maneiras de Apresentar as Recomendações para os Usuários? Um Mapeamento Sistemático da Literatura

Borba

Gasparini

2019

iSys

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Os sistemas de recomendação utilizam de informações do usuário para gerar um conjunto de itens personalizados como sugestão e são aplicados em contextos onde existe sobrecarga de conteúdo disponível ao usuário. A maneira como a visualização dessas recomendações é realizada passou a ser foco de estudos recentes conforme a necessidade de melhorar a experiência do usuário com os sistemas de recomendação. Este trabalho apresenta um mapeamento sistemático da literatura visando identificar as melhores maneiras de apresentar as recomendações para os usuários. Um total de 434 artigos foram identificados, dos quais 27 foram selecionados para análise. Os resultados apontam uma tendência para as interfaces autoexplicativas e interativas.

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An interactive interface for instilling trust and providing diverse recommendations

Cited by 9 publications

References 19 publications

Explorative Analysis of Recommendations Through Interactive Visualization

Explorative Analysis of Recommendations Through Interactive Visualization

A systematic review and taxonomy of explanations in decision support and recommender systems

Quais as Melhores Maneiras de Apresentar as Recomendações para os Usuários? Um Mapeamento Sistemático da Literatura

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