Paper Detail
Jiakai Tang, Yan Mi, Jing Yu, Yang Zhang, See-Kiong Ng, Qi Cao, Fei Sun, Xu Chen, Wen Chen, Jian Wu, Han Zhu, Bo Zheng
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made encouraging progress, reproducing a real browsing session remains difficult for two reasons. (i) Memory Challenge: a shopping session spans dozens of pages, yet existing agents either discard long-range observation histories, losing the evolving user state, or naively concatenate them, overwhelming the context window and even degrading simulation quality. (ii) Optimization Challenge: current user simulators are typically supervised to match each logged action via imitation or step-level rewards; the resulting sessions often display unrealistic patterns, such as over-exploration or excessive passivity, which per-step supervision can neither detect nor correct. To address the above challenges, we present RecVerse, a GUI-grounded simulation agent that perceives pages through screenshots and produces faithful multi-turn trajectories. For the memory challenge, RecVerse adopts a cognitive-inspired hierarchical memory: Working Memory for short-term focus, Episodic Memory for in-session traces, and Preference Memory for high-level intent, with memory updates treated as actions so that the agent adaptively learns when and what to memorize. For the optimization challenge, RecVerse is optimized with a trajectory-level RL objective that scores entire sessions, aligning both macro-level action-type distributions and micro-level shopping intent with real users. We further release USB (User Simulation Benchmark), an interactive e-commerce GUI trajectory dataset for multi-turn user simulation. Experiments show that RecVerse significantly outperforms existing baselines in both behavioral fidelity and intent consistency.
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@misc{tang2026faithful,
title = {Towards Faithful Simulation of Human Shopping Behavior},
author = {Jiakai Tang and Yan Mi and Jing Yu and Yang Zhang and See-Kiong Ng and Qi Cao and Fei Sun and Xu Chen and Wen Chen and Jian Wu and Han Zhu and Bo Zheng},
year = {2026},
abstract = {Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made encouraging progress, reproducing a real browsing session remains difficult for two reasons. (i) Memory Challenge: a shopping session spans dozens of pages, yet existing agents either discard long-range observation histories, losing the evolving user state, or naively concatenate them, overwhelming the context window and},
url = {https://huggingface.co/papers/2608.20707},
keywords = {GUI-grounded simulation agent, hierarchical memory, working memory, episodic memory, preference memory, trajectory-level RL, user simulation benchmark, huggingface daily},
eprint = {2608.20707},
archiveprefix = {arXiv},
}
{}