Persuade to Click: Context-aware Persuasion Model for Online Textual Advertisement

In recent years, due to the prevalence of online textual advertisements, increasing businesses recognize their huge potential in product promotion. As a result, persuasive tactics play an essential role in online textual advertisements, which are employed to increase the attractiveness, and sequentially increase the conversion rate and sales volume. As the context of persuasion, product attributes, e.g., category and price, also greatly influence the persuasion outcomes. However, they are largely overlooked by existing works. In this paper, we propose a novel framework to study context-aware persuasion by designing a multi-tasking learning model and performing extensive causal analysis. First, the prediction model recognizes the persuasive tactics employed in an advertising text and predicts their promotion effectiveness. Specifically, we design a disentangled representation learning algorithm to capture the persuasive tactics, and then develop a novel context-aware attention module to model the relationships between persuasive tactics and product attributes. Experiments on a large-scale real-world dataset demonstrate the superior performance of our proposed model over state-of-the-art baselines. Then we show its great practical value by conducting an in-depth causal analysis of context-aware results that our model learns, which offers insightful interpretations and guidelines for marketers to employ persuasive tactics in textual advertisements