Ad Recommendation in a Collapsed and Entangled World

arXiv:2403.00793v1 Announce Type: cross
Abstract: In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations. Our study begins by showcasing our approaches to preserving priors when encoding features of diverse types into embedding representations. Specifically, we address sequence features, numeric features, pre-trained embedding features, as well as sparse ID features. Moreover, we delve into two pivotal challenges associated with feature representation: the dimensional collapse of embeddings and the interest …

abstract arxiv attention challenges cs.ir cs.lg diverse embedding encoding features industry paper practices recommendation study type types world