Improving Collaborative Filtering Using a Cognitive Model of Human
                    Category Learning

Simone Kopeinik; Dominik Kowald; Ilire Hasani-Mavriqi; Elisabeth Lex

doi:10.1561/106.00000007

journal article Jul 24, 2017

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Simone Kopeinik Dominik Kowald Ilire Hasani-Mavriqi Elisabeth Lex

The Journal of Web Science Vol. 2 No. 1 pp. 45-61 · Emerald

View at Publisher Save 10.1561/106.00000007

Abstract

Classic resource recommenders like Collaborative Filtering treat users as just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and interpretation. SUSTAIN, as an unsupervised human category learning model, captures these dynamics. It aims to mimic a learner’s categorization behavior. In this paper, we use three social bookmarking datasets gathered from BibSonomy, CiteULike and Delicious to investigate SUSTAIN as a user modeling approach to re-rank and enrich Collaborative Filtering following a hybrid recommender strategy. Evaluations against baseline algorithms in terms of recommender accuracy and computational complexity reveal encouraging results. Our approach substantially improves Collaborative Filtering and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant. In a further step, we explore SUSTAIN’s dynamics in our specific learning task and show that both memorization of a user’s history and clustering, contribute to the algorithm’s performance. Finally, we observe that the users’ attentional foci determined by SUSTAIN correlate with the users’ level of curiosity, identified by the SPEAR algorithm. Overall, the results of our study show that SUSTAIN can be used to efficiently model attention-interpretation dynamics of users and can help improve Collaborative Filtering for resource recommendations.

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Details

Published: Jul 24, 2017
Vol/Issue: 2(1)
Pages: 45-61

Authors

S

Simone Kopeinik

Knowledge Technologies Institute, Graz University of Technology , ,

D

Dominik Kowald

Knowledge Technologies Institute, Graz University of Technology , ,; Know-Center, Graz University of Technology , ,

I

Ilire Hasani-Mavriqi

Knowledge Technologies Institute, Graz University of Technology , ,; Know-Center, Graz University of Technology , ,

E

Elisabeth Lex

Knowledge Technologies Institute, Graz University of Technology , ,

Cite This Article

Simone Kopeinik, Dominik Kowald, Ilire Hasani-Mavriqi, et al. (2017). Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning. The Journal of Web Science, 2(1), 45-61. https://doi.org/10.1561/106.00000007

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

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