The Journal of Web Science > Vol 2 > Issue 4

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Simone Kopeinik, Knowledge Technologies Institute Graz, University of Technology Graz, Austria, simone.kopeinik@tugraz.at Dominik Kowald, Knowledge Technologies Institute and Know-Center Graz, University of Technology Graz, Austria, Ilire Hasani-Mavriqi, Knowledge Technologies Institute and Know-Center Graz, University of Technology Graz, Austria, Elisabeth Lex, Knowledge Technologies Institute Graz, University of Technology Graz, Austria,
 
Suggested Citation
Simone Kopeinik, Dominik Kowald, Ilire Hasani-Mavriqi and Elisabeth Lex (2017), "Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning", The Journal of Web Science: Vol. 2: No. 4, pp 45-61. http://dx.doi.org/10.1561/106.00000007

Published: 24 Jan 2017
© 2017 S. Kopeinik, D. Kowald, I. Hasani-Mavriqi, E. Lex
 
Subjects
 
Keywords
Resource recommendationsCollaborative filtering2Hybrid recommendationsSUSTAINAttentional focusDecision makingSocial taggingLDA
 

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This is published under the terms of CC BY-NC-ND 2.0.

In this article:
1. Introduction
2. Related Work
3. Approach
4. Experimental Setup
5. Results and Discussion
6. Conclusions and Future Work
Appendix: Sustain Results for Different Numbers of LDA Topics
References
Biographies

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 hu- man 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 Filter- ing 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.

DOI:10.1561/106.00000007