Abstract
There are number of overwhelming choices over the internet therefore there is a need to filter and efficiently deliver the relevant information so that we can overcome the information overload problem, a potential problem of many internet users. Thus recommender systems provide users the solution to this problem. They search through a huge volume of dynamically generated information to provide users with their choice of content, what they want to see. Various techniques have been proposed and many software have been developed for a different no. of applications. This paper therefore reviews and explore up to date applications of recommender system, different characteristics and types of prediction techniques in recommendation systems.
Keywords
Recommender Systems
Online overload
Application development
References
- 1. Website-https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada
- 2. P. N. Vijaya Kumar A Survey on Recommender Systems (RSS) and Its Applications (2014)
- 3. Website-https://towardsdatascience.com/brief-on-recommender-systems-b86a1068a4dd
- 4. Website-http://recommender-systems.org/hybrid-recommender-systems
- 5. Website-https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada
- 6. Website-https://www.sciencedirect.com/science/article/pii/S1110866515000341#b0005
- 7. Robin Burke Hybrid Recommender Systems: Survey and Experiments
- 8. Dietmar jannach, Markus zanker Recommender Systems An Introduction
- 9. Jie Lu Recommender system application developments: A survey (2015)
- 10. Website-https://www.iteratorshq.com/blog/an-introduction-recommender-systems-9-easy-examples/