Abstract
Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.
Original language | English |
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Journal | arXiv.org e-Print archive |
Publication status | Published - 30 Jan 2015 |
Keywords
- recommender systems
- ressource recommendations
- cognitive science
- algorithm
ASJC Scopus subject areas
- Computer Science(all)
Fields of Expertise
- Information, Communication & Computing