Fifteenth ACM Conference on Recommender Systems 2021
DOI: 10.1145/3460231.3473894
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Argument-based generation and explanation of recommendations

Abstract: In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, u… Show more

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Cited by 1 publication
(3 citation statements)
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“…Paper ID F-measure [44,45,47,48,57,58,60,64,66,73,77,80,82,86] Precision [43,45,57,60,62,66,67,73,[76][77][78]80,82,86] Accuracy [43,44,46,62,63,69,70,73,[76][77][78]80,82] Recall [45,52,57,60,62,66,73,77,78,80,82,…”
Section: Discussionmentioning
confidence: 99%
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“…Paper ID F-measure [44,45,47,48,57,58,60,64,66,73,77,80,82,86] Precision [43,45,57,60,62,66,67,73,[76][77][78]80,82,86] Accuracy [43,44,46,62,63,69,70,73,[76][77][78]80,82] Recall [45,52,57,60,62,66,73,77,78,80,82,…”
Section: Discussionmentioning
confidence: 99%
“…Paper ID Deliberative [42,48,55,61,64,65,69,71,79,81,86] Democratic [41,44,50,54,65,81,85] Civic Engagement [41,44,47,[49][50][51][53][54][55]58,60,63,65,66,[68][69][70]74,76,[78][79][80][81][82][83]85,86]…”
Section: Effectsmentioning
confidence: 99%
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