Machine learning (ML) has become a bit of a buzzword in the last several years. Machine learning and artificial intelligence are now being recognized as the future of data analysis and interpretation. Because of this, it can be hard to understand the real-world specifics about how machine learning works – and what is just marketing hype.

In this article, we’ll take a look at one of the most powerful functions of machine learning; ranking. Learning to rank is one of the most important aspects of modern ML systems – and it is key to their functionality. Let’s get started! 

Machine learning algorithms don't start out “smart.” They must be educated and taught how to perform specific tasks, by feeding them data pairs and other information from databases.

 

Learning to rank (LTR) is one of these functions. Ranking algorithms are used in a huge variety of applications – from search engines, to online marketplaces, chatbots, and more.

Machine learning ranking is a method of solving ranking problems, while traditional Machine Learning solves prediction problems. 

For example, a traditional ML algorithm may be used to detect spam. The algorithm looks at the particular email or message, and searches for features that may classify it as “spam” or “not spam”.  Its goal is to come up with a single numerical score for that data point – and to classify it as one or the other.

LTR, on the other hand, is used for more complex queries and data sets. It aims to come up with an optimal ordering (ranking) of different data points, based on relevancy and other user-defined metrics. 

Take Google’s Gmail application, for example. Several years ago, it released a feature where emails could be categorized in your main inbox, or in several other inboxes labeled “Updates”, “Promotions”, and “Social”, as well as additional, user-definable categories.

This is an example of a ranking problem. Gmail likely uses a machine-learning algorithm that assesses the content – such as word use, email address from which a message is sent, HTML code, and other metrics – to determine in which inbox an email should be ranked and placed. 

Learning to rank allows algorithms to quickly determine relevance ratings and other important data metrics from a given dataset – making it a very powerful tool.

ML ranking algorithms have a wide variety of different applications. Here are just a few of them

● Online web stores – Machine ranking is often used to determine which items a user would be the most interested in, after performing a given search. For example, if a user enters “shoes” into a webshop, the ML algorithm could then serve up the most-purchased pair of shoes – ensuring a maximum chance to convert a customer, and relevant results.

● Search engines – Search engines such as Google have made use of machine learning ranking since the mid-2000s, and have continued to refine their algorithms to provide better results to users. 

● Document retrieval and storage – Machine learning adds an extra level of intelligence to document repositories and online storage databases. By analyzing queries from users, and refining the ranking system used, a document storage system can quickly begin serving up more relevant results, allowing for faster document access and retrieval.

Other areas in which machine learning ranking is used include machine translation, computational biology, and “recommender”marketplaces, chat bots, and more. or “suggested” systems often used by social media website and blogs to recommend content to users.

Algorithms get smarter as time goes on – and as more companies begin to use machine learning ranking algorithms, it’s likely that this trend will continue. So understand the basics about machine learning ranking – and how your business may be able to benefit from these “smart” algorithms.