{"type":"ScholarlyArticle","title":"Model-Free Preference Elicitation","authors":[{"name":"Carlos Martin","slug":"carlos-martin","given":"Carlos","family":"Martin","affiliation":"Carnegie Mellon University 3 Strategy Robot, Inc."},{"name":"Craig Boutilier","slug":"craig-boutilier","given":"Craig","family":"Boutilier","affiliation":"Google Research"},{"name":"Ofer Meshi","slug":"ofer-meshi","given":"Ofer","family":"Meshi","affiliation":"Google Research"},{"name":"Tuomas Sandholm","slug":"tuomas-sandholm","given":"Tuomas","family":"Sandholm","affiliation":"Carnegie Mellon University 3 Strategy Robot, Inc.; Optimized Markets, Inc.; Strategic Machine, Inc."}],"abstract":"In recommender systems, preference elicitation (PE) is an effective way to learn about a user's preferences to improve recommendation quality. \r\nExpected value of information (EVOI), a Bayesian technique that computes expected gain in user utility, has proven to be effective in selecting useful PE queries.\r\nMost EVOI methods use probabilistic models of user preferences and query responses to compute posterior utilities.\r\nBy contrast, we develop model-free variants of EVOI that rely on function approximation to obviate the need for specific modeling assumptions.\r\nSpecifically, we learn user response and utility models from existing data (often available in real-world recommender systems), which are used to estimate EVOI rather than relying on explicit probabilistic inference.\r\nWe augment our approach by using online planning, specifically, Monte Carlo tree search, to further enhance our elicitation policies.\r\nWe show that our approach offers significant improvement in recommendation quality over standard baselines on several PE tasks.","doi":"10.24963/ijcai.2024/387","url":"https://ijcai.org/proceedings/2024/387","pdf":"https://ijcai.org/proceedings/2024/0387.pdf","markdown":"https://ijcai.org/proceedings/2024/387.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/387","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":387,"pages":{"first":3493,"last":3503},"keywords":["Knowledge Representation and Reasoning: KRR: Preference modelling and preference-based reasoning","Humans and AI: HAI: Personalization and user modeling","Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2015] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, et al. 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