Contextual Advertising Strategy Generation via Attention and Interaction Guidance

Digital advertising has become one of the most important aspects of modern marketing, as it allows businesses to reach larger audiences with greater precision, controllable cost, and measurable feedback. However, the process of designing effective advertising strategies relies heavily on human experts and thus remains suboptimal. In this paper, we propose a novel method for Contextual Advertising Strategy Generation via Attention and InteRaction Guidance (ASGAR) that leverages transformers and a soft contrastive learning approach to optimize campaign performance. An advertising strategy is a combination of multiple targeting options, and its performance is tied strictly to the combination as a whole. This makes the exploration of the high combinatorial space infeasible and autoregressive methods inefficient. Therefore, constraints of non-combinatorial exploration and non-autoregressive generation have to be met. To the best of our knowledge, this is the first method that satisfies all constraints while also outperforming the previous methods. We compare our results with state-of-the-art methods on a public data set and with human experts in a company-deployed environment. We show that our method can effectively generate high-performance advertising strategies with better stability and controllable exploration.