Using General Adversarial Networks for Marketing: A Case Study of Airbnb
This work addresses marketing optimization for platforms like Airbnb, but it is incremental as it applies existing GAN techniques to a specific domain with a tailored loss function.
The paper tackled the problem of improving Airbnb listing descriptions by using GANs to replicate successful text patterns, resulting in a method that recommends rewordings to increase booking likelihood through a new DMK loss function.
In this paper, we examine the use case of general adversarial networks (GANs) in the field of marketing. In particular, we analyze how GAN models can replicate text patterns from successful product listings on Airbnb, a peer-to-peer online market for short-term apartment rentals. To do so, we define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of functions that forces the model's generated output to include a set of user-defined keywords. This allows the general adversarial network to recommend a way of rewording the phrasing of a listing description to increase the likelihood that it is booked. Although we tailor our analysis to Airbnb data, we believe this framework establishes a more general model for how generative algorithms can be used to produce text samples for the purposes of marketing.