It is widely acknowledged that large models have the potential to deliver superior
performance across a broad range of domains. Despite the remarkable progress made in …

As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention
from both academia and industry. Recent studies have been reported to establish CTR …

This paper introduces Block Data Representations (BDR), a framework for exploring and
evaluating a wide spectrum of narrow-precision formats for deep learning. It enables …

We present Tencent's ads recommendation system and examine the challenges and
practices of learning appropriate recommendation representations. Our study begins by …

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is
a fundamental task in recommender systems. The emergence of heterogeneous information …

The scale of deep learning models has grown tremendously in recent years. State-of-the-art
models have reached billions of parameters and terabyte-scale model sizes. Training of …

Effective user representations are pivotal in personalized advertising. However, stringent
constraints on training throughput, serving latency, and memory, often limit the complexity …

Search efficiency and serving efficiency are two major axes in building feature interactions
and expediting the model development process in recommender systems. Searching for the …

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and
potential for ranking systems. However, prior work focused on academic problems, which …

In this article, we present a general approach to personalizing ads through encoding and
learning from variable-length sequences of recent user actions and diverse representations …