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Meta-rules: Improving Adaptation in Recommendation Systems

dc.contributor.authorRomero, Vicentede_DE
dc.contributor.authorBurgos, Danielde_DE
dc.contributor.editorHartmann, Melaniede_DE
dc.contributor.editorHerder, Eelcode_DE
dc.contributor.editorKrause, Danielde_DE
dc.contributor.editorNauerz, Andreasde_DE
dc.date.accessioned2017-11-15T15:01:00Z
dc.date.available2017-11-15T15:01:00Z
dc.date.issued2010
dc.description.abstractRecommendation Systems are central in current applications to help the user find useful information spread in large amounts of post, videos or social networks. Most Recommendation Systems are more effective when huge amounts of user data are available in order to calculate similarities between users. Educational applications are not popular enough in order to generate large amount of data. In this context, rule-based Recommendation Systems are a better solution. Rules are in most cases written a priori by domain experts; they can offer good recommendations with even no application of usage information. However large rule-sets are hard to maintain, reengineer and adapt to user goals an preferences. Meta-rules, rules that generate rules, can generalize a rule-set providing bases for adaptation, reengineering and on the fly generation. In this paper, the authors expose the benefits of meta-rules implemented as part of a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach in rule-based Recommendation Systems.
dc.identifier.urihttp://abis.l3s.uni-hannover.de/images/proceedings/abis2010/abis4.pdfde_DE
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/5087
dc.language.isoende_DE
dc.relation.ispartof18th Intl. Workshop on Personalization and Recommendation on the Web and Beyondde_DE
dc.titleMeta-rules: Improving Adaptation in Recommendation Systemsde_DE
dc.typeText/Conference Paper
gi.document.qualitydigidoc

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