InLN: Knowledge-aware Incremental Leveling Network for Dynamic Advertising

In today's fast-paced world, advertisers are increasingly demanding real-time and accurate personalized ad delivery based on dynamic preference modeling, which emphasizes the temporality existing in both user preference and product characteristics. Meanwhile, with the development of graph neural networks (GNNs), E-commerce knowledge graphs (KG) with rich semantic relatedness are invoked to improve accuracy and provide appropriate explanations to encourage advertisers' willingness to invest in ad expenses. However, it is still challenging for existing methods to comprehensively consider both time-series interactions and graph-structured knowledge triples in a unified model, i.e., the case in knowledge-aware dynamic advertising. The interaction graph between users and products changes rapidly over time, while the knowledge in KG remains relatively stable. This results in an uneven distribution of temporal and semantic information, causing existing GNNs to fail in this scenario. In this work, we quantitatively define the above phenomenon as temporal unevenness and introduce the Incremental Leveling Network (InLN) with three novel techniques: the periodic-focusing window for node-level dynamic modeling, the biased temporal walk for subgraph-level dynamic modeling and the incremental leveling mechanism for KG updating. Verified by comprehensive and intensive experiments, InLN outperforms nine baseline models in three tasks by substantial margins, reaching up to a 9.9% improvement and averaging a 5.7% increase.