Stammdaten

Investigating the longitudinal effects of different strategies in multistakeholder recommendation scenarios with agent-based simulation
Beschreibung:

This project focuses on recommender systems with a particular emphasis on investigating the longitudinal interactions between the different stakeholders of such systems, i.e., users and recommendation service providers. Users are generally interested to receive recommendations that match their needs and interests. Recommendation providers, on the other hand, strive to sustain a business by providing recommendations that are both helpful for users and at the same time serve the organization’s goals. This work employs agent-based modelling to simulate longitudinal dynamics of recommendation systems. Understanding these dynamics shall help to build future recommender systems that are effective in the long term and support the decision-making process of recommendation service providers.

Schlagworte: Recommender systems, Agent-based Modeling and Simulation, Trust
Kurztitel: Longitudinal Effects of Recommenders
Zeitraum: 15.11.2019 - 30.10.2022
Kontakt-Email: dietmar.jannach@aau.at
Homepage: -

MitarbeiterInnen

Kategorisierung

Projekttyp laufender Arbeitsschwerpunkt
Förderungstyp Sonstiger
Forschungstyp
  • Grundlagenforschung
  • Angewandte Forschung
Sachgebiete
  • 102 - Informatik
  • 502 - Wirtschaftswissenschaften
Forschungscluster
  • Humans in the Digital Age
Genderrelevanz Genderrelevanz nicht ausgewählt
Projektfokus
  • Science to Science (Qualitätsindikator: n.a.)
Arbeitsgruppen
  • DECIDE (Decision-making in a digital environment)

Finanzierung

Keine Förderprogramme vorhanden

Kooperationen

Keine Kooperationspartner ausgewählt