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Applied Sciences
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Affective Computing and Recommender Systems
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Affective Computing and Recommender Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7992

Special Issue Editors

Dr. Yong Zheng

E-Mail Website
Guest Editor
Department of Information Technology and Management, College of Computing,Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: recommender systems; user modeling; technology-enhanced learning; fintech
Special Issues, Collections and Topics in MDPI journals
Dr. María N. Moreno García

E-Mail Website
Guest Editor
Department of Computer Science and Automation, Science Faculty, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain
Interests: data mining; web mining; machine learning; deep learning; recommender system; decision support in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emotional states can play an important role in the process of decision making. Researchers have demonstrated the impact of emotions on the effectiveness of recommender systems. Affective recommender systems (ARS) or emotion-aware recommender systems (EARS) are usually associated with multidisciplinary research, including artificial intelligence, human factors, mood or emotions, facial expressions, and physiological information with human–computer interaction.

The development of affective recommender systems promotes various research topics, such as user interaction and interfaces, algorithm design and evaluations, computational efficiency, deep learning-based recommendation models, and recommendation explanations. This Special Issue on “Affective Computing and Recommender Systems” aims to promote new theoretical models, approaches, algorithms, and applications related to ARS. Possible topics include but are not limited to:

Topics in Affective Computing

  • Emotion recognition and detection;
  • Sensing and analysis of human emotions;
  • Sentimental analysis;
  • Emotion corpora and analysis;
  • Affect-based information retrieval;
  • Affect-based decision making;
  • Affective modeling;
  • Affective analysis for human factors (e.g., personality traits, trust, etc.).

Topics in ARS/EARS

  • Novel and effective models and algorithms for ARS/EARS;
  • New approaches to utilize emotions in recommender systems;
  • Review mining or sentimental analysis to assist ARS/EARS;
  • User-centric studies and evaluations in ARS/EARS;
  • Recommendation explanations in ARS/EARS;
  • Novel applications in ARS/EARS;
  • Emotion detection or recognition in recommender systems;
  • Emotion representation or representation learning in recommender systems;
  • Novel paradigms and theoretical foundations in ARS/EARS;
  • Preference elicitation in ARS/EARS;
  • User interface design and user-adaptive interaction in ARS/EARS.

Dr. Yong Zheng
Dr. María N. Moreno García
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 submissions that pass pre-check are 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • affective computing
  • recommender systems
  • affective recommender systems
  • emotion
  • emotion-aware
  • emotion-aware recommender systems
  • human factors

Published Papers (3 papers)

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Research

24 pages, 23046 KiB  
Article
A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering
by Jin-Juan Duan, Ping-Sheng Luo, Qi Liu, Feng-Ao Sun and Li-Ming Zhu
Appl. Sci. 2023, 13(2), 710; https://doi.org/10.3390/app13020710 - 04 Jan 2023
Cited by 2 | Viewed by 1661
Abstract
Complex products (CPs) modeling design has a long development cycle and high cost, and it is difficult to accurately meet the needs of enterprises and users. At present, the Kansei Engineering (KE) method based on back-propagated (BP) neural networks is applied to solve [...] Read more.
Complex products (CPs) modeling design has a long development cycle and high cost, and it is difficult to accurately meet the needs of enterprises and users. At present, the Kansei Engineering (KE) method based on back-propagated (BP) neural networks is applied to solve the modeling design problem that meets users’ affective preferences for simple products quickly and effectively. However, the modeling feature data of CPs have a wide range of dimensions, long parameter codes, and the characteristics of time series. As a result, it is difficult for BP neural networks to recognize the affective preferences of CPs from an overall visual perception level as humans do. To address the problems above and assist designers with efficient and high-quality design, a CP modeling design method based on Long Short-Term Memory (LSTM) neural network and KE (CP-KEDL) was proposed. Firstly, the improved MA method was carried out to transform the product modeling features into feature codes with sequence characteristics. Secondly, the mapping model between perceptual images and modeling features was established based on the LSTM neural network to predict the evaluation value of the product’s perceptual images. Finally, the optimal feature sets were calculated by a Genetic Algorithm (GA). The experimental results show that the MSE of the LSTM model is only 0.02, whereas the MSE of the traditional Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) neural network models are 0.30 and 0.23, respectively. The results verified that the proposed method can effectively grapple with the CP modeling design problem with the timing factor, improve design satisfaction and shorten the R&D cycle of CP industrial design. Full article
(This article belongs to the Special Issue Affective Computing and Recommender Systems)
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34 pages, 1910 KiB  
Article
Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health
by Amelie Gyrard and Karima Boudaoud
Appl. Sci. 2022, 12(19), 9712; https://doi.org/10.3390/app12199712 - 27 Sep 2022
Cited by 3 | Viewed by 2653
Abstract
Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, [...] Read more.
Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health. Full article
(This article belongs to the Special Issue Affective Computing and Recommender Systems)
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18 pages, 4476 KiB  
Article
Investigation of Relationships between Discrete and Dimensional Emotion Models in Affective Picture Databases Using Unsupervised Machine Learning
by Marko Horvat, Alan Jović and Kristijan Burnik
Appl. Sci. 2022, 12(15), 7864; https://doi.org/10.3390/app12157864 - 05 Aug 2022
Cited by 5 | Viewed by 1974
Abstract
Digital documents created to evoke emotional responses are intentionally stored in special affective multimedia databases, along with metadata describing their semantics and emotional content. These databases are routinely used in multidisciplinary research on emotion, attention, and related phenomena. Affective dimensions and emotion norms [...] Read more.
Digital documents created to evoke emotional responses are intentionally stored in special affective multimedia databases, along with metadata describing their semantics and emotional content. These databases are routinely used in multidisciplinary research on emotion, attention, and related phenomena. Affective dimensions and emotion norms are the most common emotion data models in the field of affective computing, but they are considered separable and not interchangeable. The goal of this study was to determine whether it is possible to statistically infer values of emotionally annotated pictures using the discrete emotion model when the values of the dimensional model are available and vice versa. A positive answer would greatly facilitate stimuli retrieval from affective multimedia databases and the integration of heterogeneous and differently structured affective data sources. In the experiment, we built a statistical model to describe dependencies between discrete and dimensional ratings using the affective picture databases NAPS and NAPS BE with standardized annotations for 1356 and 510 pictures, respectively. Our results show the following: (1) there is a statistically significant correlation between certain pairs of discrete and dimensional emotions in picture stimuli, and (2) robust transformation of picture ratings from the discrete emotion space to well-defined clusters in the dimensional space is possible for some discrete-dimensional emotion pairs. Based on our findings, we conclude that a feasible recommender system for affective dataset retrieval can be developed. The software tool developed for the experiment and the results are freely available for scientific and non-commercial purposes. Full article
(This article belongs to the Special Issue Affective Computing and Recommender Systems)
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