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Helma Torkamaan

Researcher

University of Duisburg-Essen

I am a research associate and a Ph.D. candidate in the interactive systems group (computer science department) at the University of Duisburg-Essen. I study the application of recommender systems in serious domains, such as health, with the goal of improving human health and quality of life. A particular interest of mine is the intersection between computer science, behavioral science, and business, namely the application of data science in human-computer interaction (HCI).

I build systems and solutions using a highly interdisciplinary and collaborative approach to technology design that involves tools, techniques, resources, and perspectives from computer science, psychology, business, and health care. This process considers both multistakeholder and end-user needs, satisfaction, and emotional experiences. My interdisciplinary research spans recommender systems, ubiquitous computing, human-computer interaction, affective computing, positive computing, and e-health. Before joining the interactive systems group, I held various positions in industry and academia and was involved in a number of projects.

Projects

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PAX

A health recommender system app for mental health promotion

DE3P

Human-needs system for innovation and design

Mood Tracker

An android mood tracker for comparing various mood measures

PAnalytics

BMBF-funded project on Personal Analytics and Digital Health.

IWG Credibility

Defining credibility, trust, and trustworthiness in social media

UCSM

The Research Training Group “User-Centred Social Media”.

Packaging

Dimensions lead to the feeling of high quality packaging and unboxing

B2B Persona

The definition of a business customer segmentation

Smart Home

Smart Home and basic human needs, user experience.

My secure

Security products vs. Messengers and encryption

Athena

An interactive window shopping experience for entertainment

Menthal

Menthal balance project at university of Bonn on smartphone addiction.

IA

Instant Architecture, Procedural Modeling of Buldings

DRS

Daily report system in ICT, solution for knowledge management

Vacation management

Digital transformation in organizations

Meeting management

Digital transformation in organizations

PMIS

A customized project management information system

E-Health

Services for medical professionals and Telemedicine

Recent Publications

Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations (to appear)

Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item …

STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful …

Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System

Integrating behavior change theories and persuasive design principals into health recommender systems.

Towards a User Integration Framework for Personal HealthDecision Support and Recommender Systems

A multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components:Empower, Encourage, and Engage.

Selected Media Coverage

PAX Stress Management App

The PAX app as a health recommender system for mental well-being was promoted by a report and interview prepared by Gerrit Grossmass for radio 1LIVE. This radio station has a reach of up to 2.9 million daily listeners.

Test the Anti-Stress App.

Short break, long relaxation.

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Contact

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Request to download Apps, source code, or dataset for non-profit academic research purposes

To request applications, datasets, or source codes related to the publications, please send me an email. These materials are open only for non-profit academic research purposes as specified in the respective publication. In the email, you need to specify the publication that you request materials from, your academic affiliation, the aim of the project that you will use the app or dataset for, and your institutional email address.