A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. Recommender systems are utilized in a variety of areas but are most commonly recognized as playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms.
There are several approaches to building recommender systems, but most of them can be classified into two main categories:
- Content-based recommenders: These systems recommend items based on their attributes and the user’s previous ratings or actions on similar items. For example, a content-based recommender for movies might suggest a user film similar to those they have liked in the past, based on their genre, actors, and director.
- Collaborative filtering recommenders: These systems use the past interactions of a group of users to make recommendations. Collaborative filtering can be further divided into two subcategories:
- Memory-based collaborative filtering: These systems compute the similarity between items or users and make recommendations based on this similarity. For example, a user might be recommended a movie that is similar to one they have rated highly in the past, based on the ratings of other users who have rated both movies.
- Model-based collaborative filtering: These systems use machine learning algorithms to learn the patterns in the data and make predictions. For example, a model-based recommender for books might learn that users who have read certain books are likely to be interested in certain other books, and use this information to make recommendations.
- Hybrid recommenders: Some recommenders use a combination of content-based and collaborative filtering approaches to make recommendations. For example, a hybrid recommender for music might use both the genre and artist of a song, as well as the listening history of a group of users, to make recommendations.
- Cold start problem: One challenge with collaborative filtering recommenders is the “cold start” problem, which refers to the difficulty of making recommendations for new users or items that have no past interactions. One solution to this problem is to use content-based techniques to make initial recommendations until there are enough past interactions to use collaborative filtering.
- Evaluation: It is important to evaluate the performance of a recommender system to ensure that it is making accurate and useful recommendations. One common metric for evaluating the accuracy of a recommender is the Root Mean Squared Error (RMSE), which measures the difference between the predicted ratings and the actual ratings.
- I hope this helps! Do you have any other questions about recommender systems?