IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
Dian Cheng, Jiawei Chen, Wenjun Peng, YE Wen-qin, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He
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.