朝向人類購物行為的忠實模擬

摘要

模擬真實的用戶購物行為是電子商務場景中離線評估與強化學習的基礎。儘管近來基於LLM與VLM的模擬器已取得令人鼓舞的進展，要重現真實的瀏覽過程仍然存在兩大困難。(i) 記憶挑戰：一次購物過程橫跨數十個頁面，然而現有智能體若非丟棄長程觀察歷史以致失去不斷演變的用戶狀態，便是簡單地串接所有歷史，使上下文窗口不堪負荷，甚至降低模擬品質。(ii) 最佳化挑戰：目前的用戶模擬器通常透過模仿或步驟級獎勵來監督以匹配每個已記錄的動作；然而，由此產生的過程往往表現出諸如過度探索或過度被動等不真實的模式，而逐步驟的監督既無法察覺也無法修正這些問題。為了解決上述挑戰，我們提出 RecVerse，一個基於 GUI 的模擬智能體，透過螢幕截圖感知頁面，並產生忠實的多輪軌跡。針對記憶挑戰，RecVerse 採用受認知科學啟發的層級式記憶：以工作記憶處理短期焦點、情境記憶保存過程中的痕跡、偏好記憶捕捉高層意圖，並將記憶更新視為動作，使智能體能適應性地學習何時記憶以及記憶什麼。針對最佳化挑戰，RecVerse 以軌跡層級的強化學習目標進行最佳化，對完整過程進行評分，同時在宏觀層面調整動作類型分布、微觀層面對齊購物意圖，以求與真實用戶一致。我們進一步釋出 USB（User Simulation Benchmark，用戶模擬基準），一個用於多輪用戶模擬的互動式電子商務 GUI 軌跡資料集。實驗結果顯示，RecVerse 在行為真實度與意圖一致性上均顯著優於現有基準方法。

English

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.