Papers on Recommender Systems
Research papers on recommender systems can help you very quickly get up to speed on the state of the field.
Specifically, review papers that use precise language to define what a recommender system is, the algorithms that can be used, standard datasets and metrics for comparing algorithms, and hints at the state of the art techniques.
By skimming or reading a handful of review papers on recommender systems, you can quickly develop a foundation from which to dive deeper and start developing your own systems.
The field does not change that quickly, and techniques from 10 or 20 years ago will give you solid results.
Review papers on recommender systems I recommended to establish a foundational understanding include:
- Amazon.com Recommendations: Item-to-item Collaborative Filtering, 2003.
- Matrix Factorization Techniques for Recommender Systems, 2009.
- Recommender Systems, 2012.
- Recommender Systems Survey, 2013.
- Advances in Collaborative Filtering, 2015.
Once you have questions about specific techniques, you can then find papers that focus on those techniques and dive deeper.