# You Are What You Bought: Generating Customer Personas for E-commerce Applications

arXiv: https://arxiv.org/abs/2504.17304 · HTML: https://mlsanity.com/paper/2504.17304/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters](https://mlsanity.com/paper/2006.15516/) (2020)
- [Linear-Time Graph Neural Networks for Scalable Recommendations](https://mlsanity.com/paper/2402.13973/) (2024)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta](https://mlsanity.com/paper/2311.09544/) (2023)
- [Are Large Language Models a Good Replacement of Taxonomies?](https://mlsanity.com/paper/2406.11131/) (2024)
- [Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering](https://mlsanity.com/paper/2010.07042/) (2020)
- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation](https://mlsanity.com/paper/2406.14971/) (2024)
- [A Zero Attention Model for Personalized Product Search](https://mlsanity.com/paper/1908.11322/) (2019)
- [Personalized Embedding-based e-Commerce Recommendations at eBay](https://mlsanity.com/paper/2102.06156/) (2021)
- [AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations](https://mlsanity.com/paper/2403.17416/) (2024)
- [Clustered Embedding Learning for Recommender Systems](https://mlsanity.com/paper/2302.01478/) (2023)
- [Personalized PageRank to a Target Node](https://mlsanity.com/paper/1304.4658/) (2013)
- [An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce](https://mlsanity.com/paper/2403.00923/) (2024)

## Built on by (most cited first, 7 of 7)

- [OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation](https://mlsanity.com/paper/2506.05606/) (2025)
- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)
- [SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents](https://mlsanity.com/paper/2605.14205/) (2026)
- [UserGPT Technical Report](https://mlsanity.com/paper/2605.08766/) (2026)
- [Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation](https://mlsanity.com/paper/2606.01783/) (2026)
- [From "Who Is This User?" to "What Does This Purchase Mean?": A Deployed Pipeline for Semantic User Profiling at Bank Scale](https://mlsanity.com/paper/2609.19928/) (2026)
- [TRACER: Trajectory-Aligned Learning for Multi-Turn User Simulation](https://mlsanity.com/paper/2609.28690/) (2026)

## Abstract (arXiv)

In e-commerce, user representations are essential for various applications. Existing methods often use deep learning techniques to convert customer behaviors into implicit embeddings. However, these embeddings are difficult to understand and integrate with external knowledge, limiting the effectiveness of applications such as customer segmentation, search navigation, and product recommendations. To address this, our paper introduces the concept of the customer persona. Condensed from a customer's numerous purchasing histories, a customer persona provides a multi-faceted and human-readable characterization of specific purchase behaviors and preferences, such as Busy Parents or Bargain Hunters. This work then focuses on representing each customer by multiple personas from a predefined set, achieving readable and informative explicit user representations. To this end, we propose an effective and efficient solution GPLR. To ensure effectiveness, GPLR leverages pre-trained LLMs to infer personas for customers. To reduce overhead, GPLR applies LLM-based labeling to only a fraction of users and utilizes a random walk technique to predict personas for the remaining customers. We further propose RevAff, which provides an absolute error ε guarantee while improving the time complexity of the exact solution by a factor of at least O(fracε·|E|N|E|+Nlog N), where N represents the number of customers and products, and E represents the interactions between them. We evaluate the performance of our persona-based representation in terms of accuracy and robustness for recommendation and customer segmentation tasks using three real-world e-commerce datasets. Most notably, we find that integrating customer persona representations improves the state-of-the-art graph convolution-based recommendation model by up to 12% in terms of NDCG@K and F1-Score@K.