Modeling User Behavior with Graph Convolution for Personalized Product Search

TL;DR

This paper introduces a graph convolution approach to model user behavior in personalized product search, capturing complex relational patterns to improve recommendation accuracy.

Contribution

It proposes a novel user behavior graph and a jumping graph convolution method to exploit high-order relations, enhancing user preference modeling beyond existing latent space methods.

Findings

Outperforms existing methods on eight Amazon benchmarks.

Effectively captures high-order user-product relations.

Seamlessly integrates with current latent space models.

Abstract

User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at \url{https://github.com/floatSDSDS/SBG}.