SIM4REC: GENERATIVE AI LIBRARY FOR TRAINING RECOMMENDER SYSTEMS
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DESCRIPTION
SIM4REC is designed to create synthetic datasets that simulate user behavior in service provision processes (e.g., customers of banks, marketplaces, food services, and entertainment sectors). These datasets are used for training, testing, and benchmarking recommender systems. Based on aggregated data about the socio-demographic structure of users and service processes, the library outputs a trained recommender system or testing/comparison results of different systems. ADVANTAGES OF THE DEVELOPMENT
Individual user response modeling: Tailored to socio-demographic characteristics and economic conditions
Ease of adaptation: Supports diverse interaction mechanisms
Behavior prediction: Includes forecasts of user behavior changes, including in crisis scenarios
Efficiency: Reduces training time for recommender systems by 4–6 times and halves the number of users dissatisfied with recommendations during the training phase