ML Interview Question: Collaborative and content-based filtering in recommender systems?
Answers
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Recommender system is to predict whether a particular user would prefer an item or not based on the user’s profile and its historical information.
Collaborative filtering method for recommender systems is a method that is solely based on the past interactions that have been recorded between users and items, in order to produce new recommendations. Collaborative Filtering tends to find what similar users would like and the recommendations to be provided and in order to classify the users into clusters of similar types and recommend each user according to the preference of its cluster. types of collaborative filtering are user-based and Item-based Collaborative Filtering
Content-based filtering uses additional information about users and/or items. This filtering method uses item features to recommend other items similar to what the user likes and also based on their previous actions or explicit feedback.
**Differences **
- Content-based approach requires a good amount of information about items’ features, rather than using the user’s interactions and feedback.Collaborative Filtering, on the other hand, doesn’t need anything else except the user’s historical preference
- collaborative filtering model can help users discover new interests.Content-based model can only make recommendations based on the existing interests of the user and the model hence only has limited ability to expand on the users’ existing interests.
- Content-Based filtering model does not need any data about other users, since the recommendations are specific to a particular user
