{"type":"ScholarlyArticle","title":"Online Matching with Controllable Rewards and Arrival Probabilities","authors":[{"name":"Yuya Hikima","slug":"yuya-hikima","given":"Yuya","family":"Hikima","affiliation":"NTT Human Informatics Labratories , NTT Corporation ,"},{"name":"Yasunori Akagi","slug":"yasunori-akagi","given":"Yasunori","family":"Akagi","affiliation":"NTT Human Informatics Labratories , NTT Corporation ,"},{"name":"Naoki Marumo","slug":"naoki-marumo","given":"Naoki","family":"Marumo","affiliation":"NTT Communication Science Laboratories , NTT Corporation"},{"name":"Hideaki Kim","slug":"hideaki-kim","given":"Hideaki","family":"Kim","affiliation":"NTT Human Informatics Labratories , NTT Corporation ,"}],"abstract":"Online bipartite matching has attracted much attention due to its importance in various applications such as advertising, ride-sharing, and crowdsourcing. In most online matching problems, the rewards and node arrival probabilities are given in advance and are not controllable. However, many real-world matching services require them to be controllable  and the decision-maker faces a non-trivial problem of optimizing them. In this study, we formulate a new optimization problem, Online Matching with Controllable Rewards and Arrival probabilities (OM-CRA), to simultaneously determine not only the matching strategy but also the rewards and arrival probabilities. Even though our problem is more complex than the existing ones, we propose a fast 1/2-approximation algorithm for OM-CRA. The proposed approach transforms OM-CRA to a saddle-point problem by approximating the objective function, and then solves it by the Primal-Dual Hybrid Gradient (PDHG) method with acceleration through the use of the problem structure. In simulations on real data from crowdsourcing and ride-sharing platforms, we show that the proposed algorithm can find solutions with high total rewards in practical times.","doi":"10.24963/ijcai.2022/254","url":"https://ijcai.org/proceedings/2022/254","pdf":"https://ijcai.org/proceedings/2022/0254.pdf","markdown":"https://ijcai.org/proceedings/2022/254.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/254","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":254,"pages":{"first":1825,"last":1833},"keywords":["Constraint Satisfaction and Optimization: Constraint Optimization","Constraint Satisfaction and Optimization: Applications","Constraint Satisfaction and Optimization: Modeling"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986492","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984635","length_sec":352,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Alaei et al., 2012] Saeed Alaei, MohammadTaghi Haji- aghayi, and Vahid Liaghat. 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