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, pp. 447 - 450
2016book article/chapter in Proceedings
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title:
Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources
Author:
Barbu, Catalin-MihaiUDE
LSF ID
58102
Other
connected with university
Language of text:
English
Keyword, Topic:
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.