Relation-aware Heterogeneous Graph for User Profiling
This work improves user profiling for e-commerce applications by better incorporating interaction type differences, though it is incremental as it builds on existing graph-based methods.
The paper tackled the problem of user profiling by addressing the neglect of distinct interaction types in existing graph-based methods, proposing a relation-aware heterogeneous graph approach that achieved a significant performance boost on two real-world e-commerce datasets.
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.