What are the types of recommendation systems?

A recommendation system is an algorithm that intelligently suggests relevant suggestions based on specific criteria. Normally, these recommendations are popular and beneficial on e-commerce and social apps and help us conveniently find relevant products or content. There are multiple types of recommendation systems based on these apps. In this Answer, we will focus on the following types of recommendations.

Content-based filtering (CBF)

CBF makes recommendations based on the items or content attributes users visit the most. It finds similarities between different items using their attributes.

A recommender system generates a user profile based on the specific user’s visit history on a site. For example, a recommender system generates a user profile on a course hosting website using course attributes that may influence recommendations. The attributes of a course can be the following:

• Category: The course category refers to subjects such as data structure, applied maths, and so on.

• Author: This is the author of the course.

• Difficulty level: The difficulty level of the course can be easy, medium, or advanced.

Collaborative-based filtering

Collaborative-based filtering finds the similarities between the users’ behaviors instead of item attributes. For example, if User A picks the course in the specific sequence and User B also picks the course in the same order, then there is a high chance that User A also picks the same next course picked by User B, as depicted in the following diagram.

Hybrid recommendation

Hybrid recommender systems integrate multiple recommendation techniques to generate personalized recommendations by combining the advantages of many recommendation techniques, overcoming their limitations, and enhancing the relevance of recommendations. Hybrid recommendation utilizes content attributes, user behavior, social connections, and other relevant information and user feedback to predict the recommendation.

This example shows how a hybrid recommender system can combine content and collaborative filtering to provide users with a personalized course recommendation experience.

Conclusion

In this Answer, we discussed different types of recommendations. Understanding these different recommendation types can assist platforms and businesses in providing their users with more related and engaging suggestions to improve user experience and increase customer satisfaction.