Synthetic Data-Based Simulators for Recommender Systems: A Survey

• URL: http://arxiv.org/abs/2206.11338v1
• Date: Wed, 22 Jun 2022 19:33:21 GMT
• Title: Synthetic Data-Based Simulators for Recommender Systems: A Survey
• Authors: Elizaveta Stavinova, Alexander Grigorievskiy, Anna Volodkevich, Petr Chunaev, Klavdiya Bochenina, Dmitry Bugaychenko
• Abstract summary: This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation. We start with the motivation behind the development of frameworks implementing the simulations -- simulators. We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
• Score: 55.60116686945561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation (M&S) of interactions between users and recommender systems and applications of the M&S to the performance improvement of industrial recommender engines. We start with the motivation behind the development of frameworks implementing the simulations -- simulators -- and the usage of them for training and testing recommender systems of different types (including Reinforcement Learning ones). Furthermore, we provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness and moreover make a summary of the simulators found in the research literature. Besides other things, we discuss the building blocks of simulators: methods for synthetic data (user, item, user-item responses) generation, methods for what-if experimental analysis, methods and datasets used for simulation quality evaluation (including the methods that monitor and/or close possible simulation-to-reality gaps), and methods for summarization of experimental simulation results. Finally, this survey considers emerging topics and open problems in the field.

Related papers

• Embed and Emulate: Contrastive representations for simulation-based inference [11.543221890134399]
  This paper introduces Embed and Emulate (E&E), a new simulation-based inference ( SBI) method based on contrastive learning. E&E learns a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space. We demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task.
  arXiv  Detail & Related papers  (2024-09-27T02:37:01Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Addressing computational challenges in physical system simulations with machine learning [0.0]
  We present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. Our approach involves a two-step process: first, we train a supervised predictive model using a limited simulated dataset to predict simulation outcomes. Subsequently, a reinforcement learning agent is trained to generate accurate, simulation-like data by leveraging the supervised model.
  arXiv  Detail & Related papers  (2023-05-16T17:31:50Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems [5.592114738742928]
  We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems.
  arXiv  Detail & Related papers  (2021-07-19T15:16:44Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools [0.6481500397175589]
  Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks. This article investigates the applicability of GANs for imitating simulations.
  arXiv  Detail & Related papers  (2020-09-03T14:22:26Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.