NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction

TL;DR

This paper introduces NeSHFS, a heuristic feature selection algorithm designed to improve click-through rate prediction accuracy and efficiency by selecting relevant features in real-time environments.

Contribution

The paper proposes a novel heuristic-based feature selection method, NeSHFS, tailored for CTR prediction to enhance performance and reduce computational costs in live systems.

Findings

NeSHFS improves CTR prediction accuracy across multiple datasets.

NeSHFS reduces feature dimensionality and training time.

The method outperforms traditional feature selection techniques.

Abstract

Click-through-rate (CTR) prediction plays an important role in online advertising and ad recommender systems. In the past decade, maximizing CTR has been the main focus of model development and solution creation. Therefore, researchers and practitioners have proposed various models and solutions to enhance the effectiveness of CTR prediction. Most of the existing literature focuses on capturing either implicit or explicit feature interactions. Although implicit interactions are successfully captured in some studies, explicit interactions present a challenge for achieving high CTR by extracting both low-order and high-order feature interactions. Unnecessary and irrelevant features may cause high computational time and low prediction performance. Furthermore, certain features may perform well with specific predictive models while underperforming with others. Also, feature distribution may fluctuate due to traffic variations. Most importantly, in live production environments, resources are limited, and the time for inference is just as crucial as training time. Because of all these reasons, feature selection is one of the most important factors in enhancing CTR prediction model performance. Simple filter-based feature selection algorithms do not perform well and they are not sufficient. An effective and efficient feature selection algorithm is needed to consistently filter the most useful features during live CTR prediction process. In this paper, we propose a heuristic algorithm named Neighborhood Search with Heuristic-based Feature Selection (NeSHFS) to enhance CTR prediction performance while reducing dimensionality and training time costs. We conduct comprehensive experiments on three public datasets to validate the efficiency and effectiveness of our proposed solution.