Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation

• URL: http://arxiv.org/abs/2604.09549v1
• Date: Mon, 26 Jan 2026 05:01:00 GMT
• Title: Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation
• Abstract summary: We introduce ContextSim, an agent framework that simulates believable user proxies by anchoring interactions in daily life activities.<n>To align preferences with genuine humans, we model agents' internal thoughts and enforce consistency at both the action and trajectory levels.
• Score: 0.7031557790463293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains. However, their evaluation remains challenging due to the disconnect between offline metrics and online performance. The emergence of Large Language Model-powered agents offers a promising solution, yet existing studies model users in isolation, neglecting the contextual factors such as time, location, and needs, which fundamentally shape human decision-making. In this paper, we introduce ContextSim, an LLM agent framework that simulates believable user proxies by anchoring interactions in daily life activities. Namely, a life simulation module generates scenarios specifying when, where, and why users engage with recommendations. To align preferences with genuine humans, we model agents' internal thoughts and enforce consistency at both the action and trajectory levels. Experiments across domains show our method generates interactions more closely aligned with human behavior than prior work. We further validate our approach through offline A/B testing correlation and show that RS parameters optimized using ContextSim yield improved real-world engagement.

Related papers

• Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing [54.456400601801704]
  We introduce a multi-modal user agent for A/B testing (A/B Agent)<n>Specifically, we construct a recommendation sandbox environment for A/B testing, enabling multimodal and multi-page interactions.<n>We validated the potential of the agent as an alternative to traditional A/B testing from three perspectives: model, data, and features.
  arXiv  Detail & Related papers  (2026-01-08T03:33:43Z)
• AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation [0.7031557790463293]
  We introduce Align, a framework that learns world-model-driven agents from human interactions.<n>We generate counterfactual trajectories around demonstrations and prompt the LLM to compare its decisions with human choices, identify suboptimal actions, and extract lessons.
  arXiv  Detail & Related papers  (2026-01-02T03:01:33Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Dynamic benchmarking framework for LLM-based conversational data capture [0.0]
  This paper introduces a benchmarking framework to assess large language models (LLMs)<n>It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement.<n>Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
  arXiv  Detail & Related papers  (2025-02-04T15:47:47Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• Automatic Music Playlist Generation via Simulation-based Reinforcement Learning [17.628525710776877]
  Personalization of playlists is a common feature in music streaming services. We present a reinforcement learning framework that solves for user satisfaction metrics via the use of a simulated playlist-generation environment.
  arXiv  Detail & Related papers  (2023-10-13T14:13:02Z)
• Towards Data-Driven Offline Simulations for Online Reinforcement Learning [30.654163861164864]
  We formalize offline learner simulation (OLS) for reinforcement learning (RL) We propose a novel evaluation protocol that measures both fidelity and efficiency of the simulation.
  arXiv  Detail & Related papers  (2022-11-14T18:36:13Z)
• Do Offline Metrics Predict Online Performance in Recommender Systems? [79.48653445643865]
  We investigate the extent to which offline metrics predict online performance by evaluating recommenders across six simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm.
  arXiv  Detail & Related papers  (2020-11-07T01:41:13Z)

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.