Towards to Psychological-based Recommenders Systems: A survey on Recommender Systems

Authors

  • Maria Augusta Silveira Netto Nunes Universidade Federal de Sergipe

Abstract

This paper describes a brief survey of Psychological-based Recommender System describing the state of the art of the area. Firstly we briefly define the Recommender Systems, followed by the description of the approaches usually used in order to implement them. Next, we present the strengths and weaknesses of the recommendation techniques, followed by some examples.   Next, we present the preliminary work developed considering Psychological-based Recommender Systems. In this paper we detail an experiment illustrating the scenario where we apply a Personality-based Recommender System. Finally we present some conclusions.

Author Biography

Maria Augusta Silveira Netto Nunes, Universidade Federal de Sergipe

Maria Augusta Silveira Netto Nunes was born in Passo Fundo/RS, south of Brazil.
Since finishing her undergraduate studies her chosen research area have been Artificial Intelligence. 
In her Master degree, her research was focused on Cognitive Agents in order to improve the interactions between humans and computers. 
From her PhD, she began to work with Affective Computing and how to model and represent the Human Psychological aspects in computers aiming improve the personalization and satisfaction of humans during their interacting with computers.
Her more recent projects includes personalization in Recommender Systems considering Psychological aspects, such as Personality and Emotions. 

Downloads

Published

2010-09-13

How to Cite

Nunes, M. A. S. N. (2010). Towards to Psychological-based Recommenders Systems: A survey on Recommender Systems. Scientia Plena, 6(8). Retrieved from https://scientiaplena.org.br/sp/article/view/119

Issue

Vol. 6 No. 8 (2010): August/Agosto 2010

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work