Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies

Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention....

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Veröffentlicht in:	arXiv.org 2024-09
Hauptverfasser:	Hsu, Chih-Wei, Mladenov, Martin, Meshi, Ofer, Pine, James, Pham, Hubert, Li, Shane, Liang, Xujian, Polishko, Anton, Yang, Li, Scheetz, Ben, Boutilier, Craig
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Computer Science - Information Retrieval Computer Science - Learning Cycle time Experiments Policies Recommender systems Simulation Simulation models Systems analysis User behavior
Online-Zugang:	Volltext
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