Standard
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. S. 72-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10505 LNAI).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
Boubekki, A, Brefeld, U, Lucchesi, CL & Stille, W 2017,
Propagating Maximum Capacities for Recommendation. in
KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10505 LNAI, Springer, Cham, Schweiz, S. 72-84, 40th German Conference on Artificial Intelligence - KI 2017, Dortmund, Nordrhein-Westfalen, Deutschland,
25.09.17.
https://doi.org/10.1007/978-3-319-67190-1_6
APA
Boubekki, A., Brefeld, U., Lucchesi, C. L., & Stille, W. (2017).
Propagating Maximum Capacities for Recommendation. In
KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings (S. 72-84). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10505 LNAI). Springer.
https://doi.org/10.1007/978-3-319-67190-1_6
Vancouver
Boubekki A, Brefeld U, Lucchesi CL, Stille W.
Propagating Maximum Capacities for Recommendation. in KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017 : proceedings. Cham, Schweiz: Springer. 2017. S. 72-84. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67190-1_6
Bibtex
@inbook{03236f56fe2e4d0ab3a58ab6cf872160,
title = "Propagating Maximum Capacities for Recommendation",
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.",
keywords = "Business informatics, Recommender systems, Maximum capacity paths, Information propagation, Sparsity, Co-occurrence, Cold-start problem",
author = "Ahc{\`e}ne Boubekki and Ulf Brefeld and Lucchesi, {Cl{\'a}udio Leonardo} and Wolfgang Stille",
year = "2017",
doi = "10.1007/978-3-319-67190-1_6",
language = "English",
isbn = "978-3-319-67189-5",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "72--84",
booktitle = "KI 2017: Advances in Artificial Intelligence",
address = "Germany",
note = "40th German Conference on Artificial Intelligence - KI 2017, KI 2017 ; Conference date: 25-09-2017 Through 29-09-2017",
url = "http://ki2017.tu-dortmund.de",
}
RIS
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 -
