Foundations of context-aware preference propagation
March 31st, 2020
Abstract
Foundations of context-aware preference propagation is the title of the publication, which took place in January 2020, in the Journal of the ACM by Davide Martinenghi (DEIB Polimi, Paolo Ciaccia (University of Bologna), Riccardo Torlone (Roma Tre University).
The Journal of the ACM (JACM) provides coverage of the most significant work on principles of computer science, broadly construed. The scope of research covered encompasses contributions of lasting value to any area of computer science. To be accepted, a paper must be judged to be truly outstanding in its field. JACM is interested in work in core computer science and in work at the boundaries, both the boundaries of subdisciplines of computer science and the boundaries between computer science and other fields.
Abstract - Preferences are a fundamental ingredient in a variety of fields, ranging from economics to computer science, for deciding the best choices among possible alternatives. Contexts provide another important aspect to be considered in the selection of the best choices, since, very often, preferences are affected by context. In particular, the problem of preference propagation from more generic to more specific contexts naturally arises. Such a problem has only been addressed in a very limited way and always resorts to practical, ad hoc approaches. To fill this gap, in this article, we analyze preference propagation in a principled way and adopt an abstract context model without making any specific assumptions on how preferences are stated. Our framework only requires that the contexts form a partially ordered set and that preferences define a strict partial order on the objects of interest. We first formalize the basic properties that any propagation process should satisfy. We then introduce an algebraic model for preference propagation that relies on two abstract operators for combining preferences, and, under mild assumptions, we prove that the only possible interpretations for such operators are the well-known Pareto and Prioritized composition. We then study several propagation methods based on such operators and precisely characterize them in terms of the stated properties. We finally identify a method meeting all the requirements, on the basis of which we provide an efficient algorithm for preference propagation.
The article: https://doi.org/10.1145/3375713
The Journal of the ACM (JACM) provides coverage of the most significant work on principles of computer science, broadly construed. The scope of research covered encompasses contributions of lasting value to any area of computer science. To be accepted, a paper must be judged to be truly outstanding in its field. JACM is interested in work in core computer science and in work at the boundaries, both the boundaries of subdisciplines of computer science and the boundaries between computer science and other fields.
Abstract - Preferences are a fundamental ingredient in a variety of fields, ranging from economics to computer science, for deciding the best choices among possible alternatives. Contexts provide another important aspect to be considered in the selection of the best choices, since, very often, preferences are affected by context. In particular, the problem of preference propagation from more generic to more specific contexts naturally arises. Such a problem has only been addressed in a very limited way and always resorts to practical, ad hoc approaches. To fill this gap, in this article, we analyze preference propagation in a principled way and adopt an abstract context model without making any specific assumptions on how preferences are stated. Our framework only requires that the contexts form a partially ordered set and that preferences define a strict partial order on the objects of interest. We first formalize the basic properties that any propagation process should satisfy. We then introduce an algebraic model for preference propagation that relies on two abstract operators for combining preferences, and, under mild assumptions, we prove that the only possible interpretations for such operators are the well-known Pareto and Prioritized composition. We then study several propagation methods based on such operators and precisely characterize them in terms of the stated properties. We finally identify a method meeting all the requirements, on the basis of which we provide an efficient algorithm for preference propagation.
The article: https://doi.org/10.1145/3375713
