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C. Trattner, Dominik Kowald, Paul Seitlinger, Tobias Ley, Simone Kopeinik (2016)
Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking SystemsJ. Web Sci., 2
Bran Knowles, Vicki Hanson, Y. Rogers, Anne Piper, Jenny Waycott, N. Davies, Aloha Ambe, Robin Brewer, Debaleena Chattopadhyay, M. Dee, D. Frohlich, M. Gutierrez-Lopez, Benjamin Jelen, Amanda Lazar, R. Nielek, B. Pena, Abi Roper, Mark Schlager, B. Schulte, I. Yuan (2020)
The harm in conflating aging with accessibilityCommunications of the ACM, 64
T. Salthouse, R. Babcock (1991)
Decomposing adult age differences in working memory.Developmental Psychology, 27
R. Mata, Bettina Helversen, J. Rieskamp (2010)
Learning to choose: Cognitive aging and strategy selection learning in decision making.Psychology and aging, 25 2
J. Hauser (2014)
Consideration-set heuristics ☆Journal of Business Research, 67
John Anderson, M. Matessa, C. Lebiere (1997)
ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual AttentionHum. Comput. Interact., 12
B. Helversen, Katarzyna Abramczuk, Wiesław Kopeć, R. Nielek (2018)
Influence of consumer reviews on online purchasing decisions in older and younger adultsDecis. Support Syst., 113
R. Lambert-Pandraud, G. Laurent, Eric Lapersonne (2005)
Repeat Purchasing of New Automobiles by Older Consumers: Empirical Evidence and InterpretationsJournal of Marketing, 69
Nourah ALRossais (2018)
Integrating Item Based Stereotypes in Recommender SystemsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Dominik Kowald, Paul Seitlinger, C. Trattner, Tobias Ley (2013)
Long time no see: the probability of reusing tags as a function of frequency and recencyProceedings of the 23rd International Conference on World Wide Web
Jin Huang, Harrie Oosterhuis, M. Rijke, H. Hoof (2020)
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems
R. Mata, L. Schooler, J. Rieskamp (2007)
The aging decision maker: cognitive aging and the adaptive selection of decision strategies.Psychology and aging, 22 4
Cvetomir Dimov, P. Khader, Julian Marewski, Thorsten Pachur (2019)
How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-RBehavior Research Methods, 52
Clayton Stanley, M. Byrne (2016)
Comparing vector-based and Bayesian memory models using large-scale datasets: User-generated hashtag and tag prediction on Twitter and Stack Overflow.Psychological methods, 21 4
J. Cerella (1985)
Age-related decline in extrafoveal letter perception.Journal of gerontology, 40 6
Dirk Bollen, Mark Graus, M. Willemsen (2012)
Remembering the stars?: effect of time on preference retrieval from memoryProceedings of the sixth ACM conference on Recommender systems
J. Hauser, Olivier Toubia, T. Evgeniou, R. Befurt, Daria Dzyabura (2010)
Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration SetsJournal of Marketing Research, 47
R. Cabeza (2002)
Hemispheric asymmetry reduction in older adults: the HAROLD model.Psychology and aging, 17 1
Qi Ma, A. Chan, Pei-Lee Teh (2020)
Bridging the Digital Divide for Older Adults via Observational Training: Effects of Model Identity from a Generational PerspectiveSustainability, 12
T. Hess, Tara Queen, Taryn Patterson (2012)
To Deliberate or Not to Deliberate: Interactions Between Age, Task Characteristics, and Cognitive Activity on Decision Making.Journal of behavioral decision making, 25 1
Bichen Shi, Makbule Özsoy, N. Hurley, B. Smyth, E. Tragos, James Geraci, A. Lawlor (2019)
PyRecGym: a reinforcement learning gym for recommender systemsProceedings of the 13th ACM Conference on Recommender Systems
Dominik Kowald, E. Lex (2016)
The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging SystemsProceedings of the 27th ACM Conference on Hypertext and Social Media
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, Anxiang Zeng (2018)
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Gérard Chasseigne, Catherine Ligneau, Sébastien Grau, Armelle Gall, Magali Roque, E. Mullet (2004)
Aging and Probabilistic Learning in Single- and Multiple-Cue TasksExperimental Aging Research, 30
(2021)
Multiple Pathways of Cognitive Aging
W. Bruin, Andrew Parker, Baruch Fischhoff (2020)
Decision-Making Competence: More Than Intelligence?Current Directions in Psychological Science, 29
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kıcıman (2019)
Social Data: Biases, Methodological Pitfalls, and Ethical BoundariesFrontiers in Big Data, 2
Anthony Chmiel, Emery Schubert (2018)
Using Psychological Principles of Memory Storage and Preference to Improve Music Recommendation SystemsLeonardo Music Journal, 28
Lihong Li, Wei Chu, J. Langford, R. Schapire (2010)
A contextual-bandit approach to personalized news article recommendation
Min Ding, J. Hauser, Songting Dong, Daria Dzyabura, Zhilin Yang, Chenting Su, Steve Gaskin (2011)
Unstructured Direct Elicitation of Decision RulesJournal of Marketing Research, 48
E. Lex, Dominik Kowald, Paul Seitlinger, Thi Tran, A. Felfernig, M. Schedl (2021)
Psychology-informed Recommender SystemsFound. Trends Inf. Retr., 15
R. Nozari, H. Koohi (2021)
Novel implicit-trust-network-based recommendation methodologyExpert Syst. Appl., 186
G. Samanez-Larkin, S. Gibbs, Kabir Khanna, Lisbeth Nielsen, L. Carstensen, Brian Knutson (2007)
Anticipation of monetary gain but not loss in healthy older adultsNature Neuroscience, 10
Hermann (1885) (2013)
Memory: A Contribution to Experimental PsychologyAnnals of Neurosciences, 20
R. Logie, E. Maylor (2009)
An Internet study of prospective memory across adulthood.Psychology and aging, 24 3
E. Rich (1998)
User Modeling via StereotypesCogn. Sci., 3
T. Hess (2014)
Selective Engagement of Cognitive ResourcesPerspectives on Psychological Science, 9
AbstractRecommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.
Journal of Artificial Intelligence and Soft Computing Research – de Gruyter
Published: Mar 1, 2023
Keywords: recommender systems; cognitive limitations; aging; e-commerce
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