  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.
