Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Paul Seitlinger; Dominik Kowald; Simone Kopeinik; Ilire Hasani-Mavriqi; Tobias Ley; Elisabeth Lex

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Paul Seitlinger, Dominik Kowald, Simone Kopeinik, Ilire Hasani-Mavriqi, Tobias Ley, Elisabeth Lex

Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics (98770)

Research output: Contribution to journal › Article

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
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

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title = "Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics",

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.",

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author = "Paul Seitlinger and Dominik Kowald and Simone Kopeinik and Ilire Hasani-Mavriqi and Tobias Ley and Elisabeth Lex",

note = "Submitted to WWW'15 WebScience Track",

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AU - Seitlinger, Paul

AU - Kowald, Dominik

AU - Kopeinik, Simone

AU - Hasani-Mavriqi, Ilire

AU - Ley, Tobias

AU - Lex, Elisabeth

N1 - Submitted to WWW'15 WebScience Track

PY - 2015/1/30

Y1 - 2015/1/30

N2 - 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.

AB - 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.

KW - recommender systems

KW - ressource recommendations

KW - cognitive science

KW - algorithm

M3 - Article

JO - arXiv.org e-Print archive

JF - arXiv.org e-Print archive

ER -