Matrix Factorization Based Heuristics Learning for Solving Constraint Satisfaction Problems

Seda Polat Erdeniz*, Ralph Samer, Muesluem Atas

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

In configuration systems, and especially in Constraint Satisfaction Problems (CSP), heuristics are widely used and commonly referred to as variable and value ordering heuristics. The main challenges of those systems are: producing high quality configuration results and performing real-time recommendations. This paper addresses both challenges in the context of CSP based configuration tasks. We propose a novel learning approach to determine transaction-specific variable and value ordering heuristics to solve configuration tasks with high quality configuration results in real-time. Our approach employs matrix factorization techniques and historical transactions (past purchases) to learn accurate variable and value ordering heuristics. Using all historical transactions, we build a sparse matrix and then apply matrix factorization to find transaction-specific variable and value ordering heuristics. Thereafter, these heuristics are used to solve the configuration task with a high prediction quality in a short runtime. A series of experiments on real-world datasets has shown that our approach outperforms existing heuristics in terms of runtime efficiency and prediction quality.

Originalspracheenglisch
TitelFoundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
Redakteure/-innenDenis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten287-297
Seitenumfang11
ISBN (Print)9783030594909
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2020
Veranstaltung25th International Symposium on Methodologies for Intelligent Systems - TU Graz, Virtuell, Österreich
Dauer: 23 Sep 202025 Sep 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12117 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz25th International Symposium on Methodologies for Intelligent Systems
KurztitelISMIS 2020
LandÖsterreich
OrtVirtuell
Zeitraum23/09/2025/09/20

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

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