Originally published in KDNuggets, September, 2019.
Recommender systems are an important class of machine learning algorithms that offer “relevant” suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.
Practically, recommender systems encompass a class of techniques and algorithms which are able to suggest “relevant” items to users. Ideally, the suggested items are as relevant to the user as possible, so that the user can engage with those items: YouTube videos, news articles, online products, and so on.
Items are ranked according to their relevancy, and the most relevant ones are shown to the user. The relevancy is something that the recommender system must determine and is mainly based on historical data. If you’ve recently watched YouTube videos about elephants, then YouTube is going to start showing you a lot of elephant videos with similar titles and themes!
Recommender systems are generally divided into two main categories: collaborative filtering and content-based systems.
Collaborative Filtering Systems
Collaborative filtering methods for recommender systems are methods that are solely based on the past interactions between users and the target items. Thus, the input to a collaborative filtering system will be all historical data of user interactions with target items. This data is typically stored in a matrix where the rows are the users, and the columns are the items.
About the Author:
George Seif is a Machine Learning Engineer and passionate technologist. He’s driven to bringing the most cutting edge technologies to life by building real-world, applicable products. He classifies himself as a Certified Nerd.