Two-tier Graph Contextual Embedding for Cross-device User Matching

Hongren Huang, Shu Guo, Chen Li, Jiawei Sheng, Lihong Wang, Jianxin Li, Jing Liu, Shenghai Zhong

Abstract: The cross-device user matching task is to identify the behavior-logs (i.e., behavior sequences) on multiple devices that belong to one real person. Due to its anonymous and long-term properties, most previous methods of learning behavior embeddings cannot effectively capture two important features in the sequences, namely high-order connections and long-range dependencies. To this end, we propose a novel framework called Two-tier Graph Contextual Embedding (TGCE) to solve the above problems simultaneously. In the first tier, we construct behavior evolutionary graphs (BEGs) for behavior sequences and design an order-preserving neighbor aggregation network to collectively model transitions of behaviors with their neighbors. As repeated behaviors can be grouped into single nodes, our model joints neighboring environments around behaviors in a collective way, and behavior embeddings can be enriched. In the second tier, we further build scaled shortcut graphs (SSGs) by refining BEGs with random walk-based edge addition, then a position-aware graph attention network is further imposed on SSGs to facilitate fast information propagation. As distant graph nodes can be directly connected by shortcut edges, we can further capture long-range dependencies. By stacking two graph tiers, our approach can obtain graph contextual embeddings for behaviors to further improve user matching. Experimental results on the benchmark dataset show that our model outperforms various baselines in the user matching task. Our code is released on https://github.com/13061051/TGCE_2021.