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Autor*innen: Boubekki, Ahcène; Lucchesi, Claudio L.; Brefeld, Ulf; Stille, Wolfgang
Titel: Propagating maximum capacities for recommendation
Aus: Kern-Isberner, Gabriele; Fürnkranz, Johannes; Thimm, Matthias (Hrsg.): KI 2017: Advances in artificial intelligence; 40th Annual Conference on AI, Dortmund, Germany, September 25-29, 2017, proceedings, Cham: Springer, 2017 (Lecture Notes in Computer Science, 10505), S. 72-84
DOI: 10.1007/978-3-319-67190-1_6
URL: https://link.springer.com/chapter/10.1007/978-3-319-67190-1_6
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Abstract: 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. (DIPF/Orig.)
DIPF-Abteilung: Informationszentrum Bildung