Recommender systems for literature selection: A competition between decision making and memory models

Details

Serval ID
serval:BIB_D799AC238FF1
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Title
Recommender systems for literature selection: A competition between decision making and memory models
Title of the conference
Proceedings of the 31st Annual Conference of the Cognitive Science Society
Author(s)
Van Maanen L., Marewski J. N.
Publisher
Austin, TX: Cognitive Science Society
ISBN
978-1-61567-407-7
Publication state
Published
Issued date
2009
Peer-reviewed
Oui
Editor
Taatgen N. A., van Rijn H.
Pages
2914-2919
Language
english
Abstract
We examine the ability of five cognitive models to predict what publications scientists decide to read. The cognitive models are (i) the Publication Assistant, a literature recommender system that is based on a rational analysis of memory and the ACT-R cognitive architecture; (ii-iv) three simple decision heuristics, including two lexicographic ones called take-the-best and naïveLex, as well as unit-weight linear model, and (v) a more complex weighted-additive decision strategy called Franklin’s rule. In an experiment with scientists as participants, we pit these models against (vi) multiple regression. Among the cognitive models, take-the-best best predicts most scientists’ literature preferences best. Altogether, the study shows that individual differences in scientific literature selection may be accounted for by different decision-making strategies.
Keywords
Recommender system, ACT-R, Rational analysis, Simple heuristics, Take-the-best, Literature search
Create date
25/10/2011 14:41
Last modification date
20/08/2019 15:57
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