Recommender systems are used in every area of business, from online shopping to streaming services. Companies implement this technology to make suggestions based on the user’s past actions. For instance, recommended videos are the main driving force behind consumption on YouTube.
Most modern businesses today heavily rely on recommender systems to maximize revenue. Even minor improvements in the technology can generate millions of dollars in profit. That’s why good machine learning vendors have an abundance of clients.
The most successful companies today – Amazon, Netflix, Facebook, Google – are offering massive amounts of content and products for users to consume. For instance, YouTube users upload more than ~500 hours of content every minute. Organizing such a massive amount of content can be a daunting task. It is certainly impossible without the help of machine learning technology.
Let’s look at specific benefits of using recommender systems:
Personalization
Back in the old days, people were happy to visit a website and navigate to find what they were looking for. These days, users are overwhelmed with the sheer number of options at their disposal. Now businesses are expected to analyze the user’s past behaviors and make relevant suggestions. Users have become accustomed to the high standard of UX (user experience). For instance, a lot of people visit YouTube without the intention to watch a specific clip. They expect to be entertained by the platform’s suggestions.
YouTube videos don’t get most of their traffic by appearing on a homepage. Nor do they attract most viewers by being on top in the search results. Videos become popular when YouTube’s algorithm suggests them to other viewers.
Personalization is at the core of providing the best user experience. Users lean towards the websites and services that understand their needs and deliver on them.
Application of recommender systems
Ecommerce stores
Online stores utilize recommender systems in many different ways. The aim of the technology is always the same: increase the sales in one way or another. Algorithms should help users discover products that they didn’t know they needed.
Streaming platforms
Services like Netflix heavily rely on recommender systems to drive content consumption. Their platform is built around providing a personalized experience. Netflix, in particular, owes a lot of its success to the recommendation algorithms. Implementing this technology has helped them retain users on their platform.
Even when the user searches for a specific title that’s not available on Netflix, the search results show the alternatives available on the platform.
Newspapers & Blogs
Written media uses recommender systems to retain the visitor’s attention for as long as possible. When reading an article, you’ll often find links that contain related stories. I’ve often found myself going down this rabbit hole of stories that feed off on reader’s curiosity.
Establishing connections
Between user and a product
Machine learning vendors design a technology that can analyze a subset of users and detect their preferences for certain products. For instance, the user who’s curious about computers will also be interested in computer accessories. Once users’ preferences are established, eCommerce stores can start suggesting relevant products to make additional sales.
Between multiple products
During the shopping process, users are likely to come across a product they like in terms of quality, price, or technical capabilities. Once they’ve expressed a strong interest in a specific product, recommender systems can suggest very similar products for them to consider. This will help users narrow down their search and nudge them into making a decision.
Between multiple users
Recommender systems can detect similarities in the tastes of numerous users. Once the connection is established, the algorithms can suggest the content or products based on the preferences of users with similar backgrounds.
Types of recommender systems
Popularity-based – In this type of system, the recommendations are based on popularity. When users visit YouTube for the first time, their viewing history is a clean slate. In this case, it’s impossible to make suggestions based on their past actions. The best bet is to suggest videos that are most popular in the user’s location.
The downside of this approach is that it’s not personalized. Still, it’s a good starting point when there are no other options.
Content-based – This approach involves analyzing the product and finding similar products to recommend. Machine learning vendors have implemented the formulas to measure the ‘distance’ between two products based on numerical or other values.
The content-based approach doesn’t require the analysis of user’s historical data. The recommendations are based purely on product specifications.
Difficulties arise when there’s not enough information about a specific product. In some cases, the product’s features are too vague and hard to quantify.
Collaborative filtering accounts for similarities between users and their product preferences to make recommendations. This approach requires large amounts of data about users and items.
Analyzing the feedback:
Explicit
Platforms encourage users to explicitly express their feelings about a product or content. For instance, Amazon allows its customers to review a product. Netflix has a simple thumbs up/thumbs down system to let the users express their feelings. In this case, recommender systems don’t have to decipher the user’s feedback.
Implicit
Most users aren’t motivated to provide explicit feedback. Recommender systems must analyze their behavior to understand their preferences. For instance, if a Netflix subscriber watched the movie to the end, it’s a sign that the user has enjoyed it.
Where to start?
If you don’t use any solutions provided by machine learning vendors, you might be confused as to where to start.
You can start implementing recommender systems gradually. You don’t have to choose one method or the other. Most successful websites use a combination of different recommender systems to get the best results.
The first step should be to start collecting the data about the users and products.
Some businesses find a way to analyze their offline users’ preferences as well. Modern machine learning vendors offer the technology that can track a customers’ actions in a traditional store.
Conclusion
Recommender systems have played a large role in the evolution of business practices over the years. Machine learning vendors are working on perfecting the technology even further.
