# CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessment

arXiv: https://arxiv.org/abs/2407.21553 · HTML: https://mlsanity.com/paper/2407.21553/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs](https://mlsanity.com/paper/2307.03393/) (2023)
- [Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning](https://mlsanity.com/paper/2401.06805/) (2024)
- [Knowledge Graph Large Language Model (KG-LLM) for Link Prediction](https://mlsanity.com/paper/2403.07311/) (2024)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems](https://mlsanity.com/paper/2301.05544/) (2023)

## Built on by (most cited first, 8 of 8)

- [Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning](https://mlsanity.com/paper/2507.17842/) (2025)
- [Environmental Injection Attacks against GUI Agents in Realistic Dynamic Environments](https://mlsanity.com/paper/2509.11250/) (2025)
- [See, Think, Act: Online Shopper Behavior Simulation with VLM Agents](https://mlsanity.com/paper/2510.19245/) (2025)
- [SimGym: Traffic-Grounded Browser Agents for Offline A/B Testing in E-Commerce](https://mlsanity.com/paper/2602.01443/) (2026)
- [ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models](https://mlsanity.com/paper/2504.02110/) (2025)
- [SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents](https://mlsanity.com/paper/2605.19219/) (2026)
- [NextAds: Towards Next-generation Personalized Video Advertising](https://mlsanity.com/paper/2603.02137/) (2026)
- [Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation](https://mlsanity.com/paper/2604.15190/) (2026)

## Abstract (arXiv)

This paper presents the Customer Experience (CX) Simulator, a novel framework designed to assess the effects of untested web-marketing campaigns through user behavior simulations. The proposed framework leverages large language models (LLMs) to represent various events in a user's behavioral history, such as viewing an item, applying a coupon, or purchasing an item, as semantic embedding vectors. We train a model to predict transitions between events from their LLM embeddings, which can even generalize to unseen events by learning from diverse training data. In web-marketing applications, we leverage this transition prediction model to simulate how users might react differently when new campaigns or products are presented to them. This allows us to eliminate the need for costly online testing and enhance the marketers' abilities to reveal insights. Our numerical evaluation and user study, utilizing BigQuery Public Datasets from the Google Merchandise Store, demonstrate the effectiveness of our framework.