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

arXiv: https://arxiv.org/abs/2409.08703 · HTML: https://mlsanity.com/paper/2409.08703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model](https://mlsanity.com/paper/2401.10934/) (2024)
- [EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction](https://mlsanity.com/paper/2304.10711/) (2023)
- [Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation](https://mlsanity.com/paper/2108.09141/) (2021)
- [FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction](https://mlsanity.com/paper/2209.05016/) (2022)
- [MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation](https://mlsanity.com/paper/2403.06747/) (2024)
- [FLEN: Leveraging Field for Scalable CTR Prediction](https://mlsanity.com/paper/1911.04690/) (2019)
- [ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer](https://mlsanity.com/paper/2306.14392/) (2023)
- [Position bias in features](https://mlsanity.com/paper/2402.02626/) (2024)

## Abstract (arXiv)

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.