# IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search

arXiv: https://arxiv.org/abs/2202.04972 · HTML: https://mlsanity.com/paper/2202.04972/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [Hypergraph Node Representation Learning with One-Stage Message Passing](https://mlsanity.com/paper/2312.00336/) (2023)
- [Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction](https://mlsanity.com/paper/2406.19156/) (2024)
- [APeB: Benchmarking Personalization Ability of Large Language Model Agents](https://mlsanity.com/paper/2607.03162/) (2026)

## Abstract (arXiv)

A good personalized product search (PPS) system should not only focus on retrieving relevant products, but also consider user personalized preference. Recent work on PPS mainly adopts the representation learning paradigm, e.g., learning representations for each entity (including user, product and query) from historical user behaviors (aka. user-product-query interactions). However, we argue that existing methods do not sufficiently exploit the crucial collaborative signal, which is latent in historical interactions to reveal the affinity between the entities. Collaborative signal is quite helpful for generating high-quality representation, exploiting which would benefit the representation learning of one node from its connected nodes. To tackle this limitation, in this work, we propose a new model IHGNN for personalized product search. IHGNN resorts to a hypergraph constructed from the historical user-product-query interactions, which could completely preserve ternary relations and express collaborative signal based on the topological structure. On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i.e., collaborative signal) into the embedding process. It collects the information from the hypergraph neighbors and explicitly models neighbor feature interaction to enhance the representation of the target entity. Extensive experiments on three real-world datasets validate the superiority of our proposal over the state-of-the-arts.