Collaborative filtering is a technique used in the field of automated machine learning (ML) for creating effective predictive models of user data. It utilizes the fact that people tend to have similar preferences and tastes. This method is used in various applications such as online advertising, music and video services, and e-commerce.
In its simplest form, collaborative filtering is a simple recommendation system that uses data gathered from user ratings of items to generate recommendations. For example, when you purchase a book on Amazon, you will likely see recommendations for similar books based on user ratings and reviews.
The goal of collaborative filtering is to fill in the missing entries in the matrix of user-item ratings. This can be done by using various algorithms that compare user ratings with those of other users and find patterns. This data is then used to predict the ratings of items not yet rated by the user.
The main advantage of collaborative filtering is its simplicity. It requires no prior knowledge of the user and their preferences, just a set of user ratings or scores. Additionally, it does not require any parameters to be set, thus reducing the need for data preprocessing or feature engineering.
Despite its widespread use and popularity, collaborative filtering has some drawbacks. One such problem is that it can sometimes be vulnerable to data sparsity, which can lead to inaccurate or noise-compromised predictions. Furthermore, the dataset used in collaborative filtering may contain biases, leading to problems such as the filter bubble.
Collaborative filtering is an important tool for enhancing automated ML solutions. With the continuous development of ML algorithms, it is expected that collaborative filtering will be further improved.
