A conversational recommender system for diagnosis using fuzzy rules

Cordero, Pablo; Enciso, Manuel; Lopez, Daphne; Mora, Ángel

doi:10.1016/j.eswa.2020.113449

Cited by 29 publications

(5 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the characterizations of Christakopoulou et al ( 2016 ), Wu et al ( 2019 ),; Cordero et al ( 2020 ), Dong et al ( 2020 ), Zhou et al ( 2020 ), and Jannach et al ( 2021 ) conversational recommender systems are interactive systems supporting users in the navigation of the item space in an efficient fashion and recommend items based on a systematic approach of preference elicitation. Knowledge-based recommenders can be regarded as conversational since users are guided through a preference elicitation dialog (Zou et al, 2020 ).…”

Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

View full text Add to dashboard Cite

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

show abstract

Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Following the characterizations of Christakopoulou et al (2016), , Cordero et al (2020), Dong et al (2020), Zhou et al (2020), and conversational recommender systems are interactive systems supporting users in the navigation of the item space in an efficient fashion and recommend items based on a systematic approach of preference elicitation. Knowledge-based recommenders can be regarded as conversational since users are guided through a preference elicitation dialog (Zou et al, 2020).…”

Section: Conversational Recommendationmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

“…One can inspect the reduced set of implications obtained after computing the closure for S2, An extended version of the diagnosis system in this example has been presented in Cordero et al (2020), where the fcaR package has been used to build a conversational recommender system based on fuzzy rules. In that work, the recommender system designed with the help of fcaR has obtained better results than those of the classical methods used in the area of recommendations.…”

Section: Computation Of Closure and Recommendationsmentioning

confidence: 99%

fcaR, Formal Concept Analysis with R

2022

View full text Add to dashboard Cite

Formal concept analysis (FCA) is a solid mathematical framework to manage information based on logic and lattice theory. It defines two explicit representations of the knowledge present in a dataset as concepts and implications. This paper describes an R package called fcaR that implements FCA's core notions and techniques. Additionally, it implements the extension of FCA to fuzzy datasets and a simplification logic to develop automated reasoning tools. This package is the first to implement FCA techniques in R. Therefore, emphasis has been put on defining classes and methods that could be reusable and extensible by the community. Furthermore, the package incorporates an interface with the arules package, probably the most used package regarding association rules, closely related to FCA. Finally, we show an application of the use of the package to design a recommender system based on logic for diagnosis in neurological pathologies.

show abstract

A conversational recommender system for diagnosis using fuzzy rules

Cited by 29 publications

References 66 publications

Knowledge-based recommender systems: overview and research directions

Knowledge-based recommender systems: overview and research directions

Reviews in Recommender Systems: 2022

fcaR, Formal Concept Analysis with R

Contact Info

Product

Resources

About