Special Issue "Advances in Affect- and Personality-based Personalized Systems"

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

Deadline for manuscript submissions: closed (8 January 2017)

Special Issue Editors

Guest Editor
Dr. Marko Tkalčič

Faculty of Computer Science, Free University of Bolzano, Italy
Website | E-Mail
Interests: recommender systems; affective computing; affective user modeling; personality computing
Guest Editor
Dr. Berardina Nadja De Carolis

Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
Website | E-Mail
Interests: human–computer interaction; natural language generation; user modeling; agent-based systems
Guest Editor
Dr. Marco de Gemmis

Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
Website | E-Mail
Interests: intelligent information access; personalization; information retrieval; semantic web
Guest Editor
Prof. Andrej Košir

Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Website | E-Mail
Interests: telecommunication; user modeling; recommender systems; social signals; data mining

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Open AccessArticle Emotion Elicitation in a Socially Intelligent Service: The Typing Tutor
Computers 2017, 6(2), 14; doi:10.3390/computers6020014
Received: 10 January 2017 / Revised: 21 March 2017 / Accepted: 27 March 2017 / Published: 31 March 2017
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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)
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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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Open AccessArticle Assessing Efficiency of Prompts Based on Learner Characteristics
Computers 2017, 6(1), 7; doi:10.3390/computers6010007
Received: 28 November 2016 / Revised: 5 February 2017 / Accepted: 7 February 2017 / Published: 10 February 2017
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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
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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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