IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search

TL;DR

This paper introduces IHGNN, a hypergraph neural network that leverages collaborative signals from user-product-query interactions to improve personalized product search accuracy.

Contribution

The paper proposes a novel hypergraph neural network model that explicitly encodes collaborative signals from ternary relations in user interactions, enhancing representation learning for personalized search.

Findings

IHGNN outperforms state-of-the-art methods on three real-world datasets.

Explicit modeling of neighbor feature interaction improves entity representations.

Hypergraph structure effectively captures collaborative signals for better personalization.

Abstract

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.