Flexible Generation of Preference Data for Recommendation Analysis

2024-07-23

AI Summary

Realistic simulation of recommender systems requires synthetic data generation methods that faithfully reproduce both user behavioral heterogeneity and social influence effects. To address this, we propose HYDRA—a novel probabilistic generative model that jointly captures (i) user community structure (reflecting similar adoption patterns), (ii) multimodal item popularity distributions, and (iii) user engagement levels. HYDRA employs a probabilistic graphical framework to parameterize user–item interaction intensities, enabling controllable, diverse, and high-fidelity simulation of their synergistic effects. Compared to existing approaches, HYDRA significantly improves the reproducibility of social influence propagation and behavioral heterogeneity. Extensive experiments across multiple benchmark datasets demonstrate that HYDRA-generated data closely matches real-world statistics—including distributional properties, long-tail item popularity, and fine-grained interaction patterns. This makes HYDRA a reliable foundational tool for controlled analytical studies, robustness evaluation, and stress testing of recommender systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Recommender Systems
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Simulating a recommendation system in a controlled environment, to identify specific behaviors and user preferences, requires highly flexible synthetic data generation models capable of mimicking the patterns and trends of real datasets. In this context, we propose HYDRA, a novel preferences data generation model driven by three main factors: user-item interaction level, item popularity, and user engagement level. The key innovations of the proposed process include the ability to generate user communities characterized by similar item adoptions, reflecting real-world social influences and trends. Additionally, HYDRA considers item popularity and user engagement as mixtures of different probability distributions, allowing for a more realistic simulation of diverse scenarios. This approach enhances the model's capacity to simulate a wide range of real-world cases, capturing the complexity and variability found in actual user behavior. We demonstrate the effectiveness of HYDRA through extensive experiments on well-known benchmark datasets. The results highlight its capability to replicate real-world data patterns, offering valuable insights for developing and testing recommendation systems in a controlled and realistic manner. The code used to perform the experiments is publicly available at https://github.com/SimoneMungari/HYDRA.

Problem

• Generating flexible synthetic preference data for recommendation systems
• Simulating user communities with similar item adoption patterns
• Modeling item popularity and user engagement via probability distributions

Innovation

• Synthetic data generation model HYDRA
• User-item interaction, popularity, engagement factors
• Probability distributions for realistic scenario simulation