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Through numerous research programs, competitions, and economic surveys, automated information retrieval systems and recommender systems have been proven to be efficient and useful by reducing the cognitive load and time required during the search and access to data. Over the past two decades of research within this field, this improvement of human-computer interactions is mainly relying on increasing systems' accuracy at different levels. Usage mining techniques aim at inferring accurate preferences, habits, and interests and building profiles from users' actions. Collaborative and content-based filtering make use of these profiles to provide users with relevant recommendations. Ontology-based systems formally define concepts within a domain, thus reducing ambiguity.
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论文共 49 篇作者统计合作学者相似作者
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JOURNAL OF COMPUTER ASSISTED LEARNINGno. 3 (2023): 856-868
2023 ADJUNCT PROCEEDINGS OF THE 31ST ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2023 (2023): 1-6
International Conference on Language Resources and Evaluation (LREC)pp.79-85, (2022)
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adaptive and learning agents (2019)
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