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.
- Betzalel, N. D., Shapira, B., & Rokach, L. "Please, not now!" A model for timing recommendations. In Proc. RecSys '15, ACM (2015), 297--300. Google ScholarDigital Library
- Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. Understanding choice overload in recommender systems. In Proc. RecSys '10, ACM (2010), 63--70. Google ScholarDigital Library
- Bostandjiev, S., O'Donovan, J., & Höllerer, T. Tasteweights: a visual interactive hybrid recommender system. In Proc. RecSys '12, ACM (2012), 35--42. Google ScholarDigital Library
- Flanagin, A. J., & Metzger, M. J. Trusting expert-versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, 29(4), 1626--1634. Google ScholarDigital Library
- Harman, J. L., O'Donovan, J., Abdelzaher, T., & Gonzalez, C. Dynamics of human trust in recommender systems. In Proc. RecSys '14, ACM (2014), 305--308. Google ScholarDigital Library
- Harper, F. M., Xu, F., Kaur, H., Condiff, K., Chang, S., & Terveen, L. Putting users in control of their recommendations. In Proc. RecSys '15, ACM (2015), 3--10. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., & Riedl, J. Explaining collaborative filtering recommendations. In Proc. CSCW '00, ACM (2000), 241--250. Google ScholarDigital Library
- Jameson, A., Willemsen, M. C., Felfernig, A., Gemmis, M. de, Lops, P., Semeraro, G., & Chen, L. Human Decision Making and Recommender Systems. Recommender Systems Handbook, Springer US (2015), 611--648.Google Scholar
- Konstan, J. A., & Riedl, J. Recommender systems: From algorithms to user experience. User Mod. and User-Adapted Interaction 22, 1--2 (2012), 101--123. Google ScholarDigital Library
- Massa, P., & and Avesani, P. 2007. Trust-aware recommender systems. In Proc. RecSys '07. ACM (2007), 17--24. Google ScholarDigital Library
- McAuley, J., & Leskovec, J. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proc. RecSys '13, ACM (2013), 165--172. Google ScholarDigital Library
- Nanou, T., Lekakos, G., & Fouskas, K. The effects of recommendations? presentation on persuasion and satisfaction in a movie recommender system. Multimedia Systems 16, 4--5 (2010), 219--230. Google ScholarDigital Library
- Pu, P., Chen, L., & Hu, R. Evaluating recommender systems from the users' perspective: Survey of the state of the art. User Mod. and User-Adapted Interaction 22, 4--5 (2012), 317--355. Google ScholarDigital Library
- Pu, P., & Chen, L. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems, 20(6) (2007), 542--556. Google ScholarDigital Library
- Sinha, R., & Swearingen, K. The Role of Transparency in Recommender Systems. In Proc. CHI EA '02, ACM (2002), 830--831. Google ScholarDigital Library
- Tintarev, N., & and Masthoff, J. Designing and evaluating explanations for recommender systems. Recommender Systems Handbook. Springer US (2011), 479--510.Google Scholar
- Verbert, K., Parra, D., Brusilovsky, P., & Duval, E. Visualizing recommendations to support exploration, transparency and controllability. In Proc. IUI '13, ACM (2013), 351--362. Google ScholarDigital Library
Index Terms
- Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources
Recommendations
Trust and Trustworthiness in Social Recommender Systems
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceThe prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions ...
A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering
IUI '17: Proceedings of the 22nd International Conference on Intelligent User InterfacesWhile conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the ...
Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationTo increase transparency and interactive control in Recommender Systems, we extended the Matrix Factorization technique widely used in Collaborative Filtering by learning an integrated model of user-generated tags and latent factors derived from user ...
Comments