# From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations

arXiv: https://arxiv.org/abs/2402.08109 · HTML: https://mlsanity.com/paper/2402.08109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 239 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference](https://mlsanity.com/paper/2001.02772/) (2020)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [FM2: Field-matrixed Factorization Machines for Recommender Systems](https://mlsanity.com/paper/2102.12994/) (2021)
- [Similarity encoding for learning with dirty categorical variables](https://mlsanity.com/paper/1806.00979/) (2018)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions](https://mlsanity.com/paper/2106.12739/) (2021)
- [Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks](https://mlsanity.com/paper/2310.02294/) (2023)
- [Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis](https://mlsanity.com/paper/2203.05952/) (2022)
- [Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography](https://mlsanity.com/paper/2205.09301/) (2022)
- [DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record](https://mlsanity.com/paper/2303.06269/) (2023)
- [Managing dataset shift by adversarial validation for credit scoring](https://mlsanity.com/paper/2112.10078/) (2021)
- [AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2211.12105/) (2022)
- [LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation](https://mlsanity.com/paper/2310.19394/) (2023)
- [Data Preprocessing for Evaluation of Recommendation Models in E-Commerce](https://mlsanity.com/paper/1911.01273/) (2019)
- [Psychologically-Inspired Music Recommendation System](https://mlsanity.com/paper/2205.03459/) (2022)

## Abstract (arXiv)

This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of Recommendation Engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.