Propagating Maximum Capacities for Recommendation

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


Neighborhood-based approaches often fail in sparse scenarios; a direct implication for recommender systems exploiting co-occurring items is often an inappropriately poor performance. As a remedy, we propose to propagate information (e.g., similarities) across the item graph to leverage sparse data. Instead of processing only directly connected items (e.g. co-occurrences), the similarity of two items is defined as the maximum capacity path interconnecting them. Our approach resembles a generalization of neighborhood-based methods that are obtained as special cases when restricting path lengths to one. We present two efficient online computation schemes and report on empirical results.

TitelKI 2017: Advances in Artificial Intelligence : 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings
Anzahl der Seiten13
ErscheinungsortCham, Schweiz
ISBN (Print)978-3-319-67189-5
ISBN (elektronisch)978-3-319-67190-1
PublikationsstatusErschienen - 2017
Veranstaltung40th German Conference on Artificial Intelligence - KI 2017 - Technische Universität Dortmund, Dortmund, Deutschland
Dauer: 25.09.201729.09.2017
Konferenznummer: 40