Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources

Barbu, C.-M. (2016). Proceedings of the 10th ACM Conference on Recommender Systems, 447–450. ACM.


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.

Related focus areas


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Towards a Design Space for Personalizing the Presentation of Recommendations

Barbu, C.-M. and Ziegler, J. (to appear). Proceedings of the 2nd Workshop on Engineering Computer-Human Interaction in Recommender Systems.

Interactive Recommending: Framework, State of Research and Future Challenges

Loepp, B., Barbu, C.-M., and Ziegler, J. (2016). Proceedings of the Workshop on Engineering Computer-Human Interaction in Recommender Systems, 1705, 3–13.

Co-Staying: A Social Network for Increasing the Trustworthiness of Hotel Recommendations

Barbu, C.-M. and Ziegler, J. (to appear). RecTour ’17: Proceedings of the 2017 Workshop on Recommenders in Tourism.