Advances in Affect- and Personality-based Personalized Systems

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (8 January 2017) | Viewed by 14819

Special Issue Editors

Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT), University of Primorska, 6000 Koper, Slovenia
Interests: recommender systems; affective computing; affective user modeling; personality computing
Department of Computer Science, University of Bari Aldo Moro, 70121 Bari, Italy
Interests: human–computer interaction; natural language generation; user modeling; agent-based systems
Department of Computer Science, University of Bari Aldo Moro, 70121 Bari, Italy
Interests: intelligent information access; personalization; information retrieval; semantic web
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: user-adapted communications; user modeling; context-aware recommender systems; social networks; statistical signal processing; optimization of communication systems
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Special Issue Information

Dear Colleagues,

Personality and emotions shape our daily lives by having a strong influence on our preferences, decisions, and behaviour in general. In recent years, emotions and personality have shown to play an important role in various aspects of personalized systems, such as implicit feedback, contextual information, affective content labelling, cold-start problem, diversity, cross-domain recommendations, group recommendations, e-learning, conversational systems, music information retrieval, etc. With the development of robust techniques for the unobtrusive acquisition of emotions (e.g., from various modalities, such as video or physiological sensors) and personality (e.g., from social media) the time is right to take advantage of these possibilities to collect massive datasets and improve recommender systems.

We invite you to submit the outcomes of your work on the above topics to this Special Issue. The goal of the Special Issue is to make available the knowledge that builds on recent advances, such as the ones presented at the EMPIRE workshop series (https://empire2016recsys.wordpress.com/).

Dr. Marko Tkalcic
Dr. Berardina De Carolis
Dr. Marco de Gemmis
Prof. Andrej Kosir
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Usage of affect (mood/emotions) in personalization
  • Usage of personality in personalization
  • Acquisition of personality and affect for personalized systems
  • Evaluation of personalized systems based on affect and/or personality

Published Papers (2 papers)

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Research

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Article
Emotion Elicitation in a Socially Intelligent Service: The Typing Tutor
by Andrej Košir and Gregor Strle
Computers 2017, 6(2), 14; https://doi.org/10.3390/computers6020014 - 31 Mar 2017
Cited by 4 | Viewed by 7297
Abstract
This paper presents an experimental study on modeling machine emotion elicitation in a socially intelligent service, the typing tutor. The aim of the study is to evaluate the extent to which the machine emotion elicitation can influence the affective state (valence and arousal) [...] Read more.
This paper presents an experimental study on modeling machine emotion elicitation in a socially intelligent service, the typing tutor. The aim of the study is to evaluate the extent to which the machine emotion elicitation can influence the affective state (valence and arousal) of the learner during a tutoring session. The tutor provides continuous real-time emotion elicitation via graphically rendered emoticons, as an emotional feedback to learner’s performance. Good performance is rewarded by the positive emoticon, based on the notion of positive reinforcement. Facial emotion recognition software is used to analyze the affective state of the learner for later evaluation. Experimental results show the correlation between the positive emoticon and the learner’s affective state is significant for all 13 (100%) test participants on the arousal dimension and for 9 (69%) test participants on both affective dimensions. The results also confirm our hypothesis and show that the machine emotion elicitation is significant for 11 (85%) of 13 test participants. We conclude that the machine emotion elicitation with simple graphical emoticons has a promising potential for the future development of the tutor. Full article
(This article belongs to the Special Issue Advances in Affect- and Personality-based Personalized Systems)
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593 KiB  
Article
Assessing Efficiency of Prompts Based on Learner Characteristics
by Joy Backhaus, Debora Jeske, Herbert Poinstingl and Sarah Koenig
Computers 2017, 6(1), 7; https://doi.org/10.3390/computers6010007 - 10 Feb 2017
Cited by 10 | Viewed by 7081
Abstract
Personalized prompting research has shown the significant learning benefit of prompting. The current paper outlines and examines a personalized prompting approach aimed at eliminating performance differences on the basis of a number of learner characteristics (capturing learning strategies and traits). The learner characteristics [...] Read more.
Personalized prompting research has shown the significant learning benefit of prompting. The current paper outlines and examines a personalized prompting approach aimed at eliminating performance differences on the basis of a number of learner characteristics (capturing learning strategies and traits). The learner characteristics of interest were the need for cognition, work effort, computer self-efficacy, the use of surface learning, and the learner’s confidence in their learning. The approach was tested in two e-modules, using similar assessment forms (experimental n = 413; control group n = 243). Several prompts which corresponded to the learner characteristics were implemented, including an explanation prompt, a motivation prompt, a strategy prompt, and an assessment prompt. All learning characteristics were significant correlates of at least one of the outcome measures (test performance, errors, and omissions). However, only the assessment prompt increased test performance. On this basis, and drawing upon the testing effect, this prompt may be a particularly promising option to increase performance in e-learning and similar personalized systems. Full article
(This article belongs to the Special Issue Advances in Affect- and Personality-based Personalized Systems)
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