Content-based filtering creates a profile of the user and uses this profile to give relevant recommendations to the user. The profile of the user is created by the history of the user.
For example, an e-commerce company can track the following details of the user to generate recommendations:
- Items ordered in the past
- Items viewed or added to the cart but not purchased
- User browsing history to identify what kinds of products the user may be interested in
The user may or may not have manually given ratings to these items, but various factors can be considered to evaluate their relevance to the user. Based on this, new items are recommended to the user that would be interesting to that user.
The process as shown, takes the attributes ...