Propagating Maximum Capacities for Recommendation

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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 SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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 -

DOI