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RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier
TL;DR
Sequential recommender research lacks an easy way to study counterfactual and long-term user interactions from static data. The paper introduces RECSIM, a configurable platform for authoring stylized simulation environments with adjustable user, item, state-transition, and choice models. RECSIM supports controlled development and evaluation of RL/RS methods, while its environments are not intended as full-fidelity simulators or general benchmarks.
Problem
Static recommender datasets make counterfactual action effects and sequential RL evaluation difficult, especially when user states evolve over long horizons.
Method
RECSIM provides configurable environments combining user, document, choice, and transition models for sequential recommender interaction.
Results
RECSIM supports controlled development, evaluation, and comparison of sequential RL and recommender models in synthetic settings.
Takeaways & Limitations
RECSIM enables systematic exploration of recommender RL assumptions and can support academic-industrial collaboration through stylized user models.
Takeaways & Limitations
RECSIM environments are abstract and aspect-specific rather than full-fidelity simulations, and the released environments are not generally advocated as benchmarks.
Abstract
from arXiv · showhide
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.
1 Introduction
RECSIM addresses the difficulty of studying long-term, sequential recommender interactions with static logs by providing configurable simulation environments. It supports controlled RL and RS research while explicitly avoiding claims of high-fidelity simulation or universal benchmarking.
- 1 Introduction: Sequential recommenders optimize engagement or satisfaction over interaction sequences, making user-interaction dynamics central to their design.Traditional recommenders typically maximize predicted engagement myopically, whereas collaborative interactive recommenders use sequences of interactions.
- 1 Introduction: Static datasets provide limited support for RL because counterfactual action effects on user behavior are difficult to extract.Logs from myopic recommenders may also have action distributions unlike policies targeting long-term engagement, limiting off-policy methods such as inverse propensity scoring.
- 1.1 RECSIM: A Brief Sketch: RECSIM is a configurable platform for authoring synthetic recommender environments that model selected aspects of users, documents, observability, and sequential interaction.Its environments consist of user, document, and user-choice models, with configurable components and user-history summaries available to agents.
- 1.2 RECSIM and RL: RECSIM supports systematic RL and RS research across challenges including generalization across users, dynamic action spaces, partial observability, long horizons, and multiple objectives.The platform is intended to help researchers and practitioners challenge RL assumptions, refine models in simulation, and reduce live experiment cycle time.
- 1.4 Non-objectives: RECSIM is designed for stylized, aspect-specific environments rather than full-fidelity simulations or general-purpose benchmarks.The authors state that released environments are illustrations and do not advocate using them as benchmarks except for narrowly specific phenomena.
- 1.4 Non-objectives: RECSIM can facilitate academic-industrial collaboration by sharing stylized user models that convey interaction observations without exposing user data or sensitive practices.The paper presents this as a way to identify synergies between academic and industrial researchers despite limited behavioral fidelity.
2 Related Work
Related platforms established simulation as a foundation for RL evaluation and experimentation, but RECSIM emphasizes authoring configurable recommender environments for new domain characteristics. Its stylized models prioritize practical RL/RS development over high-fidelity sim-to-real transfer.
- 2.1 RL Platforms: Simulation platforms such as ALE and OpenAI Gym have supported broad evaluation and comparison of RL algorithms across environments.OpenAI Gym provides discrete and continuous environments, while ALE focuses on Atari 2600 games.
- 2.1 RL Platforms: RECSIM shares OpenAI Gym’s environment-centered design but focuses on authoring environments that expose new domain characteristics rather than primarily benchmarking agents.Configured RECSIM environments can still be wrapped in OpenAI Gym for experimentation and evaluation.
- 2.1 RL Platforms: ELF likewise supports configurable environments, hierarchy, and multi-timescale actions, but targets real-time strategy games rather than recommender systems.RECSIM differs in its focus on recommender interaction structure and configurable user behavior.
- 2.2 RS and Dialogue Simulation: RecoGym models sequential recommendation with organic navigation and intermittent recommendation, but does not allow user-state transitions and emphasizes bandit-style feedback.It does allow configuration of user response behavior and item/user dimensionality.
- 2.2 RS and Dialogue Simulation: RECSIM emphasizes easy authoring of stylized environments that reflect specific user-behavior aspects, including future integration of learned models, instead of high-fidelity sim-to-real transfer.This positioning follows earlier rule-based simulation work in recommender and dialogue settings.
3 Simulation Components
RECSIM models sequential recommendation as interactions among configurable user, document, and choice components. Its abstractions support varied user states, document features, response models, and trajectory distributions for developing recommender agents.
- Main components: RECSIM comprises user, document, and user-choice models that interact with an agent recommending document slates.The agent observes user and candidate-document features, while the environment can retain latent features and configurable history summaries.
- Document model: The document model samples latent and observable item features, while agents and users can be configured to observe different subsets.Features include document quality, topic, and global statistics such as ratings or popularity.
- User model: The user model samples configurable latent, observable, and behavioral features and includes a transition model for evolving user state.Examples include personality, satisfaction, interests, demographics, session length, visit frequency, and time budget.
- Choice model: The choice model maps slates and user features to document choices and responses, including time spent or ratings, using models such as multinomial logit and exponentiated cascade.Document quality may remain hidden until consumption and then influence state transitions.
- Trajectory model: RECSIM represents trajectories as a dynamic Bayesian network over candidate documents, slates, choices, observations, and user states.The factorization separates observation, transition, choice, policy, and document-generation components.
- Extensibility: Developers can create their own environments to stress-test recommendation algorithms against users exhibiting varied behaviors.RECSIM includes default environments and agents but explicitly encourages extensions beyond those defaults.
- SlateQ simulation environment: A configurable slate environment represents topics, document quality, length, user interests, satisfaction, evolving interests, and engagement budgets.Documents may use topic vectors or simplified one-hot topics, while satisfaction can combine user interest and document quality.
4 Software Architecture
RECSIM provides a single-user simulator, offline interaction logging, evaluation tools, Gym integration, and modular hierarchical agents. Its architecture supports configurable recommendation workflows while retaining a current limitation to sequential per-user simulation.
- Simulator control flow: The simulator cycles through user-state and candidate-document retrieval, agent slate selection, user response generation, and updates to both user state and agent policy.The user-choice model receives the full user state, whereas the agent receives only observable state and candidates.
- Simulator lifecycle: Each episode is a multi-turn interaction with one sampled user, terminating after sufficient length or a terminal user-state transition.Iterations aggregate metrics across a fixed number of turns and multiple episodes, with optional agent checkpointing.
- Simulator lifecycle: The current simulator processes users sequentially, while a planned release was intended to support interleaved user interaction.The paper explicitly identifies the sequential design as limiting.
- Batch RL: RECSIM logs simulated interaction traces as TensorFlow SequenceExamples to support offline training and subsequent environment-based evaluation of batch RL agents.This targets settings where learning from existing or legacy recommender data is necessary.
- Evaluation: TensorBoard-based evaluation aggregates reward, episode length, and diversity metrics over freshly sampled users and documents, using a separate parallel evaluation process.The system logs metrics for a specified number of episodes.
- Interfaces: RECSIM wraps environments in OpenAI Gym and exposes APIs for configurable simulation steps and reusable agent modules.The Gym interface lets developers incorporate existing RL algorithms while requiring an environment-specific reward function.
- Agent architecture: Hierarchical agent layers preprocess observations, delegate to base agents, and post-process abstract actions into concrete slates; layers can be recursively stacked.They can inject history-derived features, modify rewards for regularization, or reduce control frequency through temporal aggregation.
- Baseline agents: RECSIM includes tabular Q-learning, deep slate-Q, random, Dopamine-adapted DQN, and standard multi-armed bandit agents as baselines.Tabular Q-learning is suitable only for the smallest environments because state-action representations and slate enumeration scale exponentially or combinatorially.
5 Case Studies
RECSIM case studies show how configurable environments expose sequential recommendation challenges involving latent interests, slate action spaces, and slowly evolving user satisfaction. Across these settings, exploration, slate decomposition, and temporally aggregated policies improve long-term recommendation outcomes under the modeled assumptions.
- 5.1 Latent State Bandits: RECSIM evaluates exploration strategies when users have latent topic interests that agents must discover through interaction.The single-item environment evaluates agents using total clicks over a session.
- 5.1 Latent State Bandits: UCB1 and Q-learning perform far better than other agents in the high-affinity environment.High affinity makes topic preference more influential than document quality, increasing the value of exploration or planning.
- 5.2 Tractable Decomposition for Slate RL: Slate decomposition retains gains over myopic policies while avoiding the full cost of optimal slate search at serving time.A less costly on-policy TD-learning variant with greedy slate construction captures a significant portion of full Q-learning’s long-term engagement gains.
- 5.2 Tractable Decomposition for Slate RL: The decomposition technique remains beneficial when the assumed user choice model differs from the simulated choice model.The reported gains over myopic approaches persist under user choice model shifts.
- 5.3 Advantage Amplification over Long Horizons: Temporal aggregation improves learned policy quality in slowly evolving, low-SNR environments to nearly the level achieved with fully observed satisfaction.Temporal regularization is an alternative hierarchical layer that penalizes changes in document features.
6 Next Steps
RECSIM’s next steps target greater realism, scale, and interaction richness. Planned extensions include production-log-informed user models, concurrent execution, distributed exploration, and mixed-mode interactions across recommendation sessions.
- Model realism: The developers are fitting stylized user models to production usage logs and adding hooks for models that better reflect specific commercial recommender systems.They frame this work as aligning research objectives with realistic user needs and behaviors rather than directly solving sim-to-real transfer.
- Scalability: Concurrent execution is intended to improve simulation throughput and reflect real-world production settings.It will also enable investigation of distributed exploration across users, which the current serial control flow cannot support.
- Interaction modalities: A future release will incorporate mixed-mode interactions including preference elicitation, endorsements, navigation chips, feedback, query refinements, and natural language.The planned scope also covers users transitioning across the search-browsing spectrum over multiple sessions.