Recommender systems are the systems that are designed to recommend things to the user based on many different factors.
1. Popularity Based Recommender System:
It is a very fundamental type of recommender system which gives recommendations based on the popularity of the product. For example, the most popular product will be recommended first.
2. Collaborative-Filtering:
In general, collaborative filtering is a process of filtering information using techniques that involve multiple agents, viewpoint, data_sources etc. Specific to recommender systems, collaborative filtering is a technique to generate predictions about the interests/preferences of a user for a product based on the interests/preferences of other similar users.
The underlying assumption behind this concept is that if a person A has similar opinion as of a person B for a product, then it is very likely that A's opinion will be similar to that of B for some other product rather than that of a randomly chosen person.
The repository contains the following:
- Dataset (https://www.kaggle.com/uciml/restaurant-data-with-consumer-ratings)
- Jupyter Notebook (https://www.kaggle.com/chitwanmanchanda/recommender-system-using-surprise-library)
- README.md file