Donkers, Tim; Loepp, Benedikt; Ziegler, Jürgen:
Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control
In: Proceedings of the 24th Conference on User Modeling Adaptation and Personalization (UMAP '16) - UMAP '16 - New York, NY, USA: ACM, 2016, S. 169 - 173
2016Buchaufsatz/Kapitel in TagungsbandInformatik
Fakultät für IngenieurwissenschaftenFakultät für Ingenieurwissenschaften » Informatik und Angewandte KognitionswissenschaftFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel:
Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control
Autor(in):
Donkers, Tim; Loepp, BenediktLSF; Ziegler, JürgenLSF
DOI:
Sprache des Textes
Englisch
Schlagwort, Thema:
Recommender Systems ; Interactive Recommending ; Matrix Factorization ; Human Factors ; 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.