# Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

## TL;DR

This paper introduces a multi-task learning approach for predicting different types of conversions in online advertising, leveraging shared features and task-specific parameters to improve prediction accuracy.

## Contribution

It proposes the Multi-Task Field-weighted Factorization Machine (MT-FwFM), a novel model that jointly predicts multiple conversion types with enhanced performance.

## Key findings

- MT-FwFM improves AUC by up to 0.84% over state-of-the-art models.
- Shared feature representations benefit conversion prediction accuracy.
- Weighted AUC across all conversion types increases by 0.50%.

## Abstract

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.