IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
This work addresses the need for better personalized product search systems by improving representation learning through collaborative signals, though it appears incremental as it builds on existing representation learning paradigms.
The paper tackles the problem of insufficient exploitation of collaborative signals in personalized product search by proposing IHGNN, a model that uses a hypergraph to preserve ternary relations and an interactive hypergraph neural network to encode structure information, achieving superior performance over state-of-the-art methods on three real-world datasets.
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.