# Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

## TL;DR

This paper introduces a novel multi-task conditional attention network framework that enhances ad creative conversion prediction accuracy by addressing data imbalance and incorporating contextual attention, aiding in the creation of effective ad content.

## Contribution

It presents a new framework combining multi-task learning, conditional attention, and attention highlighting for improved ad conversion prediction and visualization.

## Key findings

- Improved conversion prediction accuracy demonstrated on real-world data.
- Conditional attention effectively incorporates genre and gender context.
- Attention highlighting visualizes key words influencing conversions.

## Abstract

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.