Accuracy- and consistency-aware recommendation of configurations

Uta, Mathias; Felfernig, Alexander; Helić, Denis; Le, Viet-Man

doi:10.1145/3546932.3546996

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…An alternative is to learn solver search heuristics in such a way that constraint solvers are enabled to predict user-relevant attribute value settings (Uta et al, 2022 ), i.e., to directly integrate recommendation knowledge into constraint solver search, for example, by applying neural networks that receive as input a set of user preferences and propose corresponding solver search heuristics [in terms of variable (value) orderings] as output. In the example shown in Table 12 , variable value orderings for the not yet instantiated variables ABtesting and license would be ABtesting [1,0] and license [100,0], indicating that the solver should first try to instantiate these variables with 1 (100) before trying other instantiations.…”

Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

“…Specifically in the context of constraint-based recommendation, multi-criteria optimization becomes an issue since (1) constraint-based recommenders are typically applied in interactive scenarios with the need of efficient solution search and (2) at the same time, solutions must be personalized, i.e., take into account the preferences of a user. Thus, constraint-based recommendation is an important application field with the need of an in-depth integration of knowledge-based reasoning and machine learning (Uta et al, 2022 ).…”

Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

“…An alternative to NN-based recommendation is to apply model-based approaches such as matrix factorization or neural networks-for related details see Erdeniz et al (2019Erdeniz et al ( , 2022 and Uta et al (2022).…”

Section: Recommending Preference Settingsmentioning

confidence: 99%

“…. Integrating knowledge-based systems with machine learning Specifically in constraint-based recommender systems, a major line of research focuses on the integration of concepts and techniques from machine learning into constraint-based reasoning, for example, by learning variable and variable value ordering heuristics (Popescu et al, 2022;Uta et al, 2022). This way, recommendation functionalities can be directly integrated with constraint solving.…”

Section: Research Directionsmentioning

confidence: 99%

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Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

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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.

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Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

Section: Recent Advances In Knowledge-based Recommendationmentioning

confidence: 99%

Section: Recommending Preference Settingsmentioning

confidence: 99%

Section: Research Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specifically in the context of constraintbased recommendation, multi-criteria optimization becomes an issue since (1) constraint-based recommenders are typically applied in interactive scenarios with the need of efficient solution search and (2) at the same time, solutions must be personalized, i.e., take into account the preferences of a user. Thus, constraintbased recommendation is an important application field with the need of an in-depth integration of knowledge-based reasoning and machine learning (Uta et al, 2022).…”

Section: Search Optimizationmentioning

confidence: 99%

Reviews in Recommender Systems: 2022

2024

Frontiers Research Topics

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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.

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Accuracy- and consistency-aware recommendation of configurations

Cited by 2 publications

References 12 publications

Knowledge-based recommender systems: overview and research directions

Knowledge-based recommender systems: overview and research directions

Reviews in Recommender Systems: 2022

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