# Modeling User Behavior with Graph Convolution for Personalized Product Search

arXiv: https://arxiv.org/abs/2202.06081 · HTML: https://mlsanity.com/paper/2202.06081/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2022.

## Built on by (most cited first, 9 of 9)

- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [Multi-Intent Attribute-Aware Text Matching in Searching](https://mlsanity.com/paper/2402.07788/) (2024)
- [Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching](https://mlsanity.com/paper/2507.05617/) (2025)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style](https://mlsanity.com/paper/2502.15616/) (2025)
- [Ontology-driven personalized information retrieval for XML documents](https://mlsanity.com/paper/2603.21139/) (2026)
- [APeB: Benchmarking Personalization Ability of Large Language Model Agents](https://mlsanity.com/paper/2607.03162/) (2026)
- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)

## Abstract (arXiv)

User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at urlhttps://github.com/floatSDSDS/SBG.