  Click-through rate (CTR) prediction is a vital task in industrial
recommendation systems. Most existing methods focus on the network architecture
design of the CTR model for better accuracy and suffer from the data sparsity
problem. Especially in industrial recommendation systems, the widely applied
negative sample down-sampling technique due to resource limitation worsens the
problem, resulting in a decline in performance. In this paper, we propose
\textbf{A}uxiliary Match \textbf{T}asks for enhancing
\textbf{C}lick-\textbf{T}hrough \textbf{R}ate prediction accuracy (AT4CTR) by
alleviating the data sparsity problem. Specifically, we design two match tasks
inspired by collaborative filtering to enhance the relevance modeling between
user and item. As the "click" action is a strong signal which indicates the
user's preference towards the item directly, we make the first match task aim
at pulling closer the representation between the user and the item regarding
the positive samples. Since the user's past click behaviors can also be treated
as the user him/herself, we apply the next item prediction as the second match
task. For both the match tasks, we choose the InfoNCE as their loss function.
The two match tasks can provide meaningful training signals to speed up the
model's convergence and alleviate the data sparsity. We conduct extensive
experiments on one public dataset and one large-scale industrial recommendation
dataset. The result demonstrates the effectiveness of the proposed auxiliary
match tasks. AT4CTR has been deployed in the real industrial advertising system
and has gained remarkable revenue.
