Recommender systems are one of the most widespread and successful machine learning applications in business. In recent years, they have become more and more constant in our lives with the rise of Amazon, Netflix, YouTube, and plenty of others.
You’ve probably come across the line “Customers who purchased this item also looked at”. That’s one prime example of a recommender system in all its glory. But who uses them and how effective is it?
Excel With Machine Learning
Many companies are excelling in this day and age by introducing AI into their business. Large corporations use recommenders to help their customers discover the latest products relevant to their preferred choices.
It creates a fantastic user experience. Not only that, but it also improves revenue for businesses in the process.
Away from the giants of the corporate world, other industries can adopt the same approach and enjoy an equal success. Take Tata Motors, for example.
No traffic congestion, no accidents, and possibly in the future for Tata Motors, no driver. Many commuters are already carpooling and sharing journeys with colleagues over buying a vehicle.
It won’t be long before everyone follows in Google and Tesla’s footsteps to produce self-driving cars. Self-driving cars using AI can make use of cognitive equipment and sensors to drive without any concerns.
This will allow them to avoid traffic and accidents. It also takes away the human element; which is excellent in situations where we might freeze occasionally and are unable to make a split-second decision.
For many businesses in the coming years, AI will be decisive for competitiveness. Companies that will successfully take advantage of this unique technology will be able to increase their edge over competitors.
LeoVegas with their brand LeoVegas Sport in India is just one of many companies that have already taken advantage of this technology in the country. It is fully using AI to enhance their business and improve customer experience.
Johan Bjurgert, Head of Data Science at LeoVegas, commented about the technology on their blog page: “This is opening up new opportunities that we previously couldn’t have imagined. By processing large volumes of data we can discover entirely new ways; to perform work tasks and thereby dramatically boost productivity. Over time we will be able to revolutionise things that we previously took for granted; such as how we conduct scientific research.”
Bjurgert also outlined his desire to improve the LeoVegas customer experience through recommender systems: “We have developed algorithmic support for personal game recommendations, which improves the gaming experience, as customers are offered game recommendations that are personalised to their specific tastes. The work on evaluating and improving recommendations is just one of many activities in predictive analysis…”
The Two Main Approaches
Recommender systems are an advantageous alternative to search algorithms. This is because they assist customers and help them uncover information and items that they probably wouldn’t have found with them.
The algorithms in recommender systems are generally separated into two categories – collaborative filtering and content-based. Both approaches are often present in modern recommenders.
Collaborative filtering looks at a customer’s past behaviour. In contrast, content-based filtering uses a combination of different characteristics of an item so that the artificial intelligence (AI) can recommend additional items with similar properties.
A good example is Netflix. They use both the collaborative and content-based approach when filtering a customer’s TV and film tastes. They can make predictions on which program or film a customer should like based on what they’ve watched previously. This is a much more personal approach.
It eliminates the need to recommend shows that simply have the highest number of votes but don’t relate to that particular customer’s interests. They’ll also be checking the movie tooltip, vertical and downward scrolling as well as search.
Companies need to be careful when solely using content-based recommenders as they have their limitations. They are not great at capturing complex behaviours.
For example, a reader on this site may enjoy reading articles on Machine Learning, but only when they include practical examples and data rather than just a theory based argument. These types of recommenders alone cannot capture specific information like this.
To conclude, companies can now introduce machine learning technology into many different scenarios with ease, as long as there are funds available to do so.
Not only does it enhance a customer’s experience and give them a more in-depth insight into specific areas, but it’s also a catalyst for increased revenue. Tour de France organisers, the Amaury Sport Organisation, did this well a few years ago and many have followed suit since in different capacities.
With technology continually evolving, expect machine learning to grow exponentially in the future and expect recommender systems to become the norm in a business environment.