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Rational, emotional, and attentional models for recommender systems
Author(s) -
Almomani Ameed,
Monreal Cristina,
Sieira Jorge,
Graña Juan,
Sánchez Eduardo
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12594
Subject(s) - computer science , gaze , task (project management) , process (computing) , recommender system , machine learning , artificial intelligence , cognitive psychology , human–computer interaction , psychology , management , economics , operating system
This work analyses the decision‐making process underlying choice behaviour. First, neural and gaze activity were recorded experimentally from different subjects performing a choice task in a Web Interface. Second, choice models and ensembles were fitted using rational, emotional, and attentional features. The model's predictions were evaluated in terms of their accuracy and rankings were made for each user. The results show that (a) the attentional models are the best in terms of its average performance across all users, (b) each subject shows a different best model, and (c) ensembles may perform better than single choice models but an optimal building method has to be found.