Propagating Maximum Capacities for Recommendation
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Propagating Maximum Capacities for Recommendation. / Boubekki, Ahcène; Brefeld, Ulf; Lucchesi, Cláudio Leonardo et al.
KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings. Cham, Schweiz : Springer, 2017. p. 72-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10505 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Propagating Maximum Capacities for Recommendation
AU - Boubekki, Ahcène
AU - Brefeld, Ulf
AU - Lucchesi, Cláudio Leonardo
AU - Stille, Wolfgang
N1 - Conference code: 40
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Business informatics
KW - Recommender systems
KW - Maximum capacity paths
KW - Information propagation
KW - Sparsity
KW - Co-occurrence
KW - Cold-start problem
UR - http://www.scopus.com/inward/record.url?scp=85030871909&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67190-1_6
DO - 10.1007/978-3-319-67190-1_6
M3 - Article in conference proceedings
AN - SCOPUS:85030871909
SN - 978-3-319-67189-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 84
BT - KI 2017: Advances in Artificial Intelligence
PB - Springer
CY - Cham, Schweiz
T2 - 40th German Conference on Artificial Intelligence - KI 2017
Y2 - 25 September 2017 through 29 September 2017
ER -