Despite the rapid growth of online advertisement in developing countries,
existing highly over-parameterized Click-Through Rate (CTR) prediction models
are difficult to be deployed due to the limited computing resources. In this
paper, by bridging the relationship between CTR prediction task and tabular
learning, we present that tabular learning models are more efficient and
effective in CTR prediction than over-parameterized CTR prediction models.
Extensive experiments on eight public CTR prediction datasets show that tabular
learning models outperform twelve state-of-the-art CTR prediction models.
Furthermore, compared to over-parameterized CTR prediction models, tabular
learning models can be fast trained without expensive computing resources
including high-performance GPUs. Finally, through an A/B test on an actual
online application, we show that tabular learning models improve not only
offline performance but also the CTR of real users.

本研究旨在通过建立点击率预测任务与表格学习之间的联系，证明表格学习模型在 CTR 预测中比超参数化 CTR 预测模型更高效、更有效，并通过 8 个公共 CTR 预测数据集上的实验以及实际在线应用的 A / B 测试，证明这种模型不仅可以提高离线性能，还可以提高真实用户的 CTR。

通过 Tabular Learning 实现发展中国家的高效点击率预测

Efficient Click-Through Rate Prediction for Developing Countries via  Tabular Learning

Motivated by modern applications, such as online advertisement and
recommender systems, we study the top-$k$ extreme contextual bandits problem,
where the total number of arms can be enormous, and the learner is allowed to
select $k$ arms and observe all or some of the rewards for the chosen arms. We
first propose an algorithm for the non-extreme realizable setting, utilizing
the Inverse Gap Weighting strategy for selecting multiple arms. We show that
our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log
(|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is
the class containing the regression function, while only requiring
$\tilde{O}(A)$ computation per time step. In the extreme setting, where the
total number of arms can be in the millions, we propose a practically-motivated
arm hierarchy model that induces a certain structure in mean rewards to ensure
statistical and computational efficiency. The hierarchical structure allows for
an exponential reduction in the number of relevant arms for each context, thus
resulting in a regret guarantee of $O(k\sqrt{(\log A-k+1)T \log
(|\mathcal{F}|T)})$. Finally, we implement our algorithm using a hierarchical
linear function class and show superior performance with respect to well-known
benchmarks on simulated bandit feedback experiments using extreme multi-label
classification datasets. On a dataset with three million arms, our reduction
scheme has an average inference time of only 7.9 milliseconds, which is a 100x
improvement.

研究了上下文臂问题，其中臂的总数可以巨大，并允许学习者选择 k 个臂并观察所选臂的所有或某些收益。我们为非极端现实设置提出了一个算法，并提出了一种实用的臂层次结构模型来处理极端情况，这种层次结构允许每个上下文的相关臂数指数减少，从而获得了有关遗憾的保证。