Relation-aware Heterogeneous Graph for User Profiling

TL;DR

This paper introduces a relation-aware heterogeneous graph model utilizing transformer-based message passing to improve user profiling by capturing diverse interaction types, leading to significant performance improvements on e-commerce datasets.

Contribution

The paper proposes a novel relation-aware heterogeneous graph approach with transformer-based message passing for more effective user profiling.

Findings

Significant performance boost on two real-world e-commerce datasets.

Effective modeling of diverse interaction types improves user profile representations.

Transformer-based message passing enhances entity interactions in heterogeneous graphs.

Abstract

User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entities of a graph and turn the problem into a node classification task. However, they neglect the difference of distinct interaction types, e.g. user clicks an item v.s.user purchases an item, and thus cannot incorporate such information well. To solve these issues, we propose to leverage the relation-aware heterogeneous graph method for user profiling, which also allows capturing significant meta relations. We adopt the query, key, and value mechanism in a transformer fashion for heterogeneous message passing so that entities can effectively interact with each other. Via such interactions on different relation types, our model can generate representations with rich information for the user profile prediction. We conduct experiments on two real-world e-commerce datasets and observe a significant performance boost of our approach.