# Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

arXiv: https://arxiv.org/abs/1712.07525 · HTML: https://mlsanity.com/paper/1712.07525/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2017.

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

- [A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities](https://mlsanity.com/paper/2111.14247/) (2021)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [Learning to Rank with Small Set of Ground Truth Data](https://mlsanity.com/paper/2207.01188/) (2022)
- [RECOMED: A Comprehensive Pharmaceutical Recommendation System](https://mlsanity.com/paper/2301.00280/) (2022)
- [Multi-BERT for Embeddings for Recommendation System](https://mlsanity.com/paper/2308.13050/) (2023)

## Abstract (arXiv)

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.