Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

TL;DR

This paper introduces deep learning models that automatically learn feature interactions from multi-field categorical data to improve user response prediction, outperforming existing models in large-scale experiments.

Contribution

The paper proposes two novel deep neural network models combined with feature transformation methods like FMs, RBMs, and DAEs for effective user response prediction from categorical data.

Findings

Models outperform state-of-the-art methods in large-scale experiments.

Deep models effectively learn feature interactions without manual feature engineering.

Feature transformation methods improve training efficiency and prediction accuracy.

Abstract

Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results in a heavy computation in the large feature space. To tackle the issue, we propose two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks. To get our DNNs efficiently work, we propose to leverage three feature transformation methods, i.e., factorisation machines (FMs), restricted Boltzmann machines (RBMs) and denoising auto-encoders (DAEs). This paper presents the structure of our models and their efficient training algorithms. The large-scale experiments with real-world data demonstrate that our methods work better than major state-of-the-art models.