Recommendation systems are another example of AI technology that has been weaved into our everyday lives. Amazon, YouTube, Netflix, LinkedIn, and Facebook all rely on recommendation technology and we don't even realize we are using it. Recommendation systems rely heavily on data and the more data that is at their disposable, the more powerful they become. It is not coincidence that these companies have some of the biggest market caps in the world and their power comes from them being able to harness the hidden power in their customer's data. Expect this trend to continue in the future.
What is a recommendation? Let's answer the question by first exploring what it is not. It is not a definitive answer. Certain questions like "what is two plus two?" or "how many moons does Saturn have?" have a definite answer and there is no room for subjectivity. Other questions like "what is your favorite movie?" or "do you like radishes?" are completely subjective and the answer is going to depend on the person answering the question. Some machine learning algorithms thrive with this kind of
"fuzziness." Again, these recommendations can have tremendous implications.
Think of the consequences of Amazon constantly recommending a product versus another. The company that makes the recommended product will thrive and the company that makes the product that was not recommended could go out of business if it doesn't find alternative ways to distribute and sell its product.
One of the ways that a recommender system can improve is by having previous selections from users of the system. If you visit an e-commerce site for the first time and you don't have an order history, the site will have a hard time making a recommendation tailored to you. If you purchase sneakers, the website now has one data point that it can start using as a starting point. Depending on the sophistication of the system, it might recommend a different pair of sneakers, a pair of athletic socks, or maybe even a basketball (if the shoes were high-tops).
An important component of good recommendation systems is a randomization factor that occasionally "goes out on a limb" and makes oddball recommendations that might not be that related to the initial user's choices. Recommender systems don't just learn from history to find similar recommendations, but they also attempt to make new recommendations that might not be related at first blush. For example, a Netflix user might watch "The Godfather" and Netflix might start recommending Al Pacino movies or mobster movies. But it might recommend "Bourne Identity," which is a stretch. If the user does not take the recommendation or does not watch the movie, the algorithm will learn from this and avoid other movies like the "Bourne Identity" (for example any movies that have Jason Bourne as the main character).
As recommender systems get better, the possibilities are exciting. They will be able to power personal digital assistants and become your personal butler that has intimate knowledge of your likes and dislikes and can make great suggestions that you might have not thought about. Some of the areas where recommendations can benefit from these systems are:
• Restaurants
• Movies
• Music
• Potential partners (online dating)
• Books and articles
• Search results
• Financial services (robo-advisors)
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