{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/decomposition-strategies-for-constructive","title":"Decomposition Strategies for Constructive Preference Elicitation","arxiv_id":"1711.08247","date":"2017-11-22","proceeding":null,"authors":["Paolo Dragone","Stefano Teso","Mohit Kumar","Andrea Passerini"],"abstract":"We tackle the problem of constructive preference elicitation, that is the\nproblem of learning user preferences over very large decision problems,\ninvolving a combinatorial space of possible outcomes. In this setting, the\nsuggested configuration is synthesized on-the-fly by solving a constrained\noptimization problem, while the preferences are learned itera tively by\ninteracting with the user. Previous work has shown that Coactive Learning is a\nsuitable method for learning user preferences in constructive scenarios. In\nCoactive Learning the user provides feedback to the algorithm in the form of an\nimprovement to a suggested configuration. When the problem involves many\ndecision variables and constraints, this type of interaction poses a\nsignificant cognitive burden on the user. We propose a decomposition technique\nfor large preference-based decision problems relying exclusively on inference\nand feedback over partial configurations. This has the clear advantage of\ndrastically reducing the user cognitive load. Additionally, part-wise inference\ncan be (up to exponentially) less computationally demanding than inference over\nfull configurations. We discuss the theoretical implications of working with\nparts and present promising empirical results on one synthetic and two\nrealistic constructive problems.","url_abs":"http://arxiv.org/abs/1711.08247v2","url_pdf":"http://arxiv.org/pdf/1711.08247v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"decomposition-strategies-for-constructive","repo_url":"https://github.com/unitn-sml/pcl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}