Business Case: Recommendation Systems Powered by AI—Still Room for Improvement

 
Business Case: Recommendation Systems Powered by AI—Still Room for Improvement
Much has been written about the wonders of some of the most well-known recommendation systems in use today at companies like Amazon, Netflix, LinkedIn, Facebook, and YouTube. These recommendations are credited with giving their companies a significant competitive advantage and are said to be responsible for significant increases in whatever system the company uses to keep score. For Amazon, that would be sales dollars. The Amazon recommendation system is said to be responsible for 35% of sales, a figure that has been cited by several authors dating back to at least 2013 (MacKenzie, Meyer, & Noble, 2013; Morgan, 2018). The Netflix recommendation system is also believed to be one of the best in the business. Netflix counts success in terms of how many shows people watch, how much time they spend watching Netflix, and other metrics associated with engagement and time on channel. But the Netflix recommendation system is also credited with moving dollars to the company’s bottom line to the tune of $1 billion a year (Arora, 2016).
In the realm of social media, score is kept a little differently, and in the case of Facebook and LinkedIn, recommendation systems are frequently used to suggest connections you might wish to add to your network. Facebook periodically show you friends of friends that you might be interested in “friending,” while on LinkedIn, you are frequently shown the profiles of individuals that might make great professional connections. Finally, YouTube’s recommendation system lines up a queue of videos that stand ready to fill your viewing screen once your current video finishes playing. Sometimes the relationship between your current video and the line-up of recommended videos is obvious. While watching a clip of a Saturday Night Live sketch, you can see that several of the recommended videos waiting for you are also SNL clips. But not always, and that is probably where some cool recommendation engine juju comes into play, trying to figure out what will really grab your interest and keep you on-site for a few more minutes, watching new clips and the increasingly annoying advertisements that now seem to find multiple ways of popping up and interrupting your use of YouTube’s platform without paying the price of admission.
While all of these companies are to be credited for pioneering recommendation technology that most likely generates beneficial results, it seems that more often than not, the recommendations we get are not as impressive as what so many blog writers would have us believe.
Today, all these recommendation systems have been infused and super-charged from their original creations with the power of artificial intelligence.
Answer the following questions:
Has this really changed much in terms of the user experience?
How many times do you really send a friend request to that person Facebook tells you that you share four friends in common?
Would you accept a friend request from that individual if they sent one to you?
How often do you try to connect with the professionals that LinkedIn recommends to you?
Or do you find the whole process of deleting all those suggestions a pain?
Finally, how often have you sat down to watch Netflix, and after scrolling through all their movies and television shows, you end up watching another channel or maybe decide to go read a book?
Or when was the last time you purchased an unsolicited product that was recommended to you on Amazon?
Your paper should be 3 to 4 pages long using APA format. Provide appropriate citations