# A Survey of Recommender System Techniques and the Ecommerce Domain

arXiv: https://arxiv.org/abs/2208.07399 · HTML: https://mlsanity.com/paper/2208.07399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 77 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Dynamic Tensor Recommender Systems](https://mlsanity.com/paper/2003.05568/) (2020)
- [A Context-Aware User-Item Representation Learning for Item Recommendation](https://mlsanity.com/paper/1712.02342/) (2017)
- [Explainable Entity-based Recommendations with Knowledge Graphs](https://mlsanity.com/paper/1707.05254/) (2017)
- [A multi-level collaborative filtering method that improves recommendations](https://mlsanity.com/paper/1804.08891/) (2018)

## Built on by (most cited first, 1 of 1)

- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)

## Abstract (arXiv)

In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them find the information they are looking for. In recent years, a research field has emerged known as recommender systems. Recommenders have become important as they have many real-life applications. This paper reviews the different techniques and developments of recommender systems in e-commerce, e-tourism, e-resources, e-government, e-learning, and e-library. By analyzing recent work on this topic, we will be able to provide a detailed overview of current developments and identify existing difficulties in recommendation systems. The final results give practitioners and researchers the necessary guidance and insights into the recommendation system and its application.