  We present Tencent's ads recommendation system and examine the challenges and
practices of learning appropriate recommendation representations. Our study
begins by showcasing our approaches to preserving prior knowledge when encoding
features of diverse types into embedding representations. We specifically
address sequence features, numeric features, and pre-trained embedding
features. Subsequently, we delve into two crucial challenges related to feature
representation: the dimensional collapse of embeddings and the interest
entanglement across different tasks or scenarios. We propose several practical
approaches to address these challenges that result in robust and disentangled
recommendation representations. We then explore several training techniques to
facilitate model optimization, reduce bias, and enhance exploration.
Additionally, we introduce three analysis tools that enable us to study feature
correlation, dimensional collapse, and interest entanglement. This work builds
upon the continuous efforts of Tencent's ads recommendation team over the past
decade. It summarizes general design principles and presents a series of
readily applicable solutions and analysis tools. The reported performance is
based on our online advertising platform, which handles hundreds of billions of
requests daily and serves millions of ads to billions of users.
