Recommender System

 Recommender System:

Recommender Systems are the software agents that elicit the interests and preferences of individual consumers and make recommendations accordingly. It is the system used according to the user based preferences and most of them are from online stores providing recommendations.

There are two basic approaches of Recommender System. They are:

Collaborative Filtering:

This filtering is used according to the model based, which might have a single user behaviour or from other users who has the similar traits. The collaborative filtering is mostly used as group knowledge to form a recommendation based with like users.

Advantages of collaborating filtering: 

- It can be used by large , commercial e-commerce sites and applicable in many domains.

- It uses the wisdom of the crowd to recommend items.

- The users give rating to catalogue items and some customer have similar taste in past, will have the similarity in future as well.

Disadvantage of collaborative filtering:

- There must be enough users in the system for the match.

- If there are many items to be recommended with many users then the user/ratings matrix is sparse. Then, it will be hard to find the users that have rated the same items.

- It cannot recommend the item that has not been rated previously.

- It cannot recommend items to someone with unique taste.

Content-based Filtering:

This filtering is mostly based on the content of items rather than the users opinions. Some of the applications are: Newsweeder and,  Syskill and Webert.

Advantages of  Content-based filtering:

- No need of data based on other users.

- Able to recommend with users with unique taste.

- Able to recommend and explain new and unpopular item according to the content features.

Disadvantage of Content-based filtering:

- Content are required which an be encoded as meaningful feature.

- Unable to exploit quality judgment of other users.

Hybrid approach:

The combination of collaborative filtering and content-based filtering are the complexity of recommender system which is known as hybrid approach.

 Why the Recommender System is used?

For user:

- To help users to find the interesting things and set many choices.

- To help users to explore there opinions in various terms.

From provider:

- To make unique personalised service for users.

- To know more about users opinion and knowledge.

- To build up the trust and loyalty with the users.


At last, Recommender System is mostly used or important in online store or, website to help or upgrade them to experience new things and be involved in new ideas.


Here, In this lesson we used the Anaconda Navigator to run all the code with python 3.







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