Content-based filtering is a recommendation technique that suggests items to users based on the specific features of the items and the preferences expressed by the user. This approach focuses on analyzing the content of items—such as text, keywords, metadata, or attributes—to create a profile for each user. For example, if a user enjoys action movies starring a particular actor, the system can recommend more movies with that same actor or similar genres. It essentially builds a user’s profile based on their past interactions and the characteristics of the items they have liked.
In contrast, collaborative filtering relies on user interaction and behavior as a way to recommend items. Instead of examining the content of items, this method analyzes patterns from multiple users. It identifies users with similar tastes and preferences and recommends items that other like-minded users have enjoyed. For instance, if User A and User B both liked the same five movies, and User B also enjoyed a sixth movie that User A hasn’t seen, the system would recommend that sixth movie to User A. Collaborative filtering works well when there is a large dataset of user interactions but can struggle in situations where user data is sparse.
The main difference between these two methods lies in their data sources. Content-based filtering leans on the attributes of the items themselves and the individual user’s preferences, while collaborative filtering relies on the collective input and behaviors of various users. This leads to strengths and weaknesses for each approach. Content-based filtering is generally effective for suggesting niche items suitable for an individual, whereas collaborative filtering can introduce users to popular or trending items based on community behavior but can also face challenges with new users or items, known as the "cold start" problem.