Barbu, Catalin-Mihai:
Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources
In: Proceedings of the 10th ACM Conference on Recommender Systems - RecSys'16 - ACM, 2016, S. 447 - 450
2016Buchaufsatz/Kapitel in TagungsbandInformatikrecommender systemsuser interfacestrust
Fakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel:
Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources
Autor(in):
Barbu, Catalin-MihaiLSF
DOI:
WWW URL
Sprache des Textes
Englisch
Schlagwort, Thema:
recommender systems

Abstract:

Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process--which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced--as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user's situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.