# A Survey of Retrieval Algorithms in Ad and Content Recommendation Systems

arXiv: https://arxiv.org/abs/2407.01712 · HTML: https://mlsanity.com/paper/2407.01712/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing](https://mlsanity.com/paper/2404.19543/) (2024)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Utilizing Large Language Models for Information Extraction from Real Estate Transactions](https://mlsanity.com/paper/2404.18043/) (2024)

## Built on by (most cited first, 5 of 5)

- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering](https://mlsanity.com/paper/2407.08916/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter](https://mlsanity.com/paper/2408.14013/) (2024)
- [RaCT: Ranking-aware Chain-of-Thought Optimization for LLMs](https://mlsanity.com/paper/2412.14405/) (2024)

## Abstract (arXiv)

This survey examines the most effective retrieval algorithms utilized in ad recommendation and content recommendation systems. Ad targeting algorithms rely on detailed user profiles and behavioral data to deliver personalized advertisements, thereby driving revenue through targeted placements. Conversely, organic retrieval systems aim to improve user experience by recommending content that matches user preferences. This paper compares these two applications and explains the most effective methods employed in each.