Modeling Activation Processes in Human Memory for Tag Recommendations

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The thesis explores the intersection of cognitive processes and social tagging systems, focusing on how users assign tags to resources. It critiques existing tag recommendation algorithms for their data-driven nature and proposes a cognitive-inspired approach based on the ACT-R model, which simulates memory activation. The findings reveal that understanding these activation processes enhances both social tag and hashtag recommendations. This research lays the groundwork for future studies on designing recommender systems that incorporate cognitive principles.