| Title | Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Donkers, Tim, Loepp, Benedikt, Ziegler, Jürgen |
| Conference Name | Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4368-8 |
| Keywords | adaptive filtering, human factors, interactive recommending, matrix factorization, pubcrawl, recommender systems, Resiliency, scalabilty, tags, user experience |
| Abstract | To 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 ratings. Our approach enables users to manipulate their preference profile expressed implicitly in the (intransparent) factor space through explicitly presented tags. Furthermore, it seems helpful in cold-start situations since user preferences can be elicited via meaningful tags instead of ratings. We evaluate this approach and present a user study that to our knowledge is the most extensive empirical study of tag-enhanced recommending to date. Among other findings, we obtained promising results in terms of recommendation quality and perceived transparency, as well as regarding user experience, which we analyzed by Structural Equation Modeling. |
| URL | http://doi.acm.org/10.1145/2930238.2930287 |
| DOI | 10.1145/2930238.2930287 |
| Citation Key | donkers_tag-enhanced_2016 |
Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control