Personalized Image Retrieval with Sparse Graph Representation Learning [conference paper]

Conference

26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - August 23, 2020

Authors

Xiaowei Jia (Ph.D. 2020), Handong Zhao, Zhe Lin, Ajinkya Kale, Vipin Kumar (professor)

Abstract

Personalization is essential for enhancing the customer experience in retrieval tasks. In this paper, we develop a novel method CA-GCN for personalized image retrieval in the Adobe Stock image system. The proposed method CA-GCN leverages user behavior data in a Graph Convolutional Neural Network (GCN) model to learn user and image embeddings simultaneously. Standard GCN performs poorly on sparse user-image interaction graphs due to the limited knowledge gain from less representative neighbors. To address this challenge, we propose to augment the sparse user-image interaction data by considering the similarities among images. Specifically, we detect clusters of similar images and introduce a set of hidden super-nodes in the graph to represent clusters. We show that such an augmented graph structure can significantly improve the retrieval performance on real-world data collected from Adobe Stock service. In particular, when testing the proposed method on real users' stock image retrieval sessions, we get promoted average click position from 70 to 51.

Link to full paper

Personalized Image Retrieval with Sparse Graph Representation Learning

Keywords

data mining, machine learning