Itqualityassurance
Content-based filtering is a type of recommender system that attempts to guess what a user may like based on that user's activity. Content-based filtering makes recommendations by using keywords and attributes assigned to objects in a database (e.g., items in an online marketplace) and matching them to a user profile.
- Why use content based filtering?
- Which algorithm is used in content based filtering?
- What is a content based recommender system?
- What is content based processing?
Why use content based filtering?
The model doesn't need any data about other users, since the recommendations are specific to this user. This makes it easier to scale to a large number of users. The model can capture the specific interests of a user, and can recommend niche items that very few other users are interested in.
Which algorithm is used in content based filtering?
Content-based filtering in recommender systems leverages machine learning algorithms to predict and recommend new but similar items to the user.
What is a content based recommender system?
Content-based Recommender System
Here, the system uses your features and likes in order to recommend you with things that you might like. It uses the information provided by you over the internet and the ones they are able to gather and then they curate recommendations according to that.
What is content based processing?
The Content-based approach requires a good amount of information about items' features, rather than using the user's interactions and feedback. They can be movie attributes such as genre, year, director, actor etc. or textual content of articles that can be extracted by applying Natural Language Processing.
