Matrix Factorization Based Heuristics Learning for Solving Constraint Satisfaction Problems

Seda Polat Erdeniz*, Ralph Samer, Muesluem Atas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
EditorsDenis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030594909
Publication statusPublished - 1 Jan 2020
Event25th International Symposium on Methodologies for Intelligent Systems - TU Graz, Virtuell, Austria
Duration: 23 Sept 202025 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12117 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Symposium on Methodologies for Intelligent Systems
Abbreviated titleISMIS 2020

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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