SIMILARITY AND NOVELTY METRICS: A MACHINE LEARNING FRAMEWORK FOR AUDIENCE EXTENSION

In the domain of digital advertising, a principal imperative is the precise identification and engagement of a target audience—comprising both extant consumers identified from historical data and potential prospects convertible into future patrons. A persistent and substantive challenge in this endeavor lies in constructing targeting constraints that not only capture existing behavioral patterns but also extrapolate toward highpropensity yet unobserved audience segments. This strategic expansion, commonly designated as audience extension, has conventionally been addressed through greedy cover algorithms, which prioritize audience volume to the exclusion of nuanced performance indicators. In this study, we present a methodological augmentation of the greedy framework by incorporating dual performance metrics—similarity and novelty—as evaluative criteria. The proposed algorithm introduces a multi-objective optimization framework that facilitates the judicious expansion of audience segments while preserving representational fidelity to the original cohort. We empirically substantiate the efficacy of our framework through multiple case studies, demonstrating its superiority in balancing quantitative performance with qualitative audience alignment.