Comparing genetic algortihms and matrix factorization for learning heuristics in constraint solving

Knowledge-based recommender systems assist users in the active configuration of complex products. These systems rely on solving Constraint Satisfaction Problems (CSP). In constraint solving, variable and value ordering heuristics help to increase efficiency. Applying such heuristics can increase the performance of CSP solvers. On the other hand, if we apply specific heuristics to similar CSPs, CSP solver performance could be further improved. In previous work, we have proposed novel approaches to learn such heuristics, however, an evaluation in terms of consistency and prediction quality is still lacking. In this paper, we evaluate an compare two proposed approaches to learn heuristics, one relying on Genetic Algorithms and Clustering, and one on Matrix Factorization, on the same problem. Our results provide valuable insights for future research in this domain.