# Flexible Generation of Preference Data for Recommendation Analysis

arXiv: https://arxiv.org/abs/2407.16594 · HTML: https://mlsanity.com/paper/2407.16594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Graph Condensation for Graph Neural Networks](https://mlsanity.com/paper/2110.07580/) (2021)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [Multi-Component Graph Convolutional Collaborative Filtering](https://mlsanity.com/paper/1911.10699/) (2019)
- [Privacy-Preserving Synthetic Data Generation for Recommendation Systems](https://mlsanity.com/paper/2209.13133/) (2022)
- [Review-LLM: Harnessing Large Language Models for Personalized Review Generation](https://mlsanity.com/paper/2407.07487/) (2024)
- [Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective](https://mlsanity.com/paper/2003.02474/) (2020)
- [Scalable Realistic Recommendation Datasets through Fractal Expansions](https://mlsanity.com/paper/1901.08910/) (2019)
- [Real-time Attention Based Look-alike Model for Recommender System](https://mlsanity.com/paper/1906.05022/) (2019)
- [The Amplification Paradox in Recommender Systems](https://mlsanity.com/paper/2302.11225/) (2023)
- [Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding](https://mlsanity.com/paper/2008.03797/) (2020)
- [Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems](https://mlsanity.com/paper/2311.03488/) (2023)
- [Generation Meets Recommendation: Proposing Novel Items for Groups of Users](https://mlsanity.com/paper/1808.01199/) (2018)

## Built on by (most cited first, 2 of 2)

- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems](https://mlsanity.com/paper/2602.16315/) (2026)

## Abstract (arXiv)

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.