Complete Playlist of Unsupervised Machine Learning https://www.youtube.com/playlist?list=PLfQLfkzgFi7azUjaXuU0jTqg03kD-ZbUz

Even though recommender systems have been very profitable for some businesses, that happens, some use cases that have left people and society at large worse off. However, you use recommender systems or for that matter other learning algorithms, I hope you only do things that make society at large and people better off. Let's take a look at some of the problematic use cases of recommender systems, as well as ameliorations to reduce harm or to increase the amount of good that they can do. As you've seen in the last few videos, there are many ways of configuring a recommender system. When we saw binary labels, the label y could be, does a user engage or did they click or did they explicitly like an item? When designing a recommender system, choices in setting the goal of the recommender system and a lot of choices and deciding what to recommend to users. For example, you can decide to recommend to users movies most likely to be rated five stars by that user. That seems fine. That seems like a fine way to show users movies that they would like. Or maybe you can recommend to the user products that they are most likely to purchase. That seems like a very reasonable use of a recommender system as well. Versions of recommender systems can also be used to decide what ads to show to a user. One thing you could do is to recommend or really to show to the user as the most likely to be clicked on. Actually, what many companies will do is try to show as likely to click on and where the advertiser had put in a high bid because for many ad models, the revenue that the company collects depends on whether the ad was clicked on and what the advertiser had bid per-click. While this is a profit-maximizing strategy, there are also some possible negative implications of this type of advertising. I'll give a specific example on the next slide. One other thing that many companies do is try to recommend products that generate the largest profit. If you go to a website and search for a product today, there are many websites that are not showing you the most relevant product or the product that you are most likely to purchase. But is instead trying to show you the products that will generate the largest profit for the company. If a certain product is more profitable for them, because they can buy it more cheaply and sell it at a higher price, that gets ranked higher in the recommendations. Now, many companies view a pressure to maximize profit. This doesn't seem like an unreasonable thing to do but on the flip side, from the user perspective, when a website recommends to you a product, sometimes it feels it could be nice if the website was transparent with you about the criteria by which it is deciding what to show you. Is it trying to maximize their profits or trying to show you things that are most useful to you? On video websites or social media websites, a recommender system can also be modified to try to show you the content that leads to the maximum watch time. Specifically, websites that are an ad revenue tend to have an incentive to keep you on the website for a long time. Trying to maximize the time you spend on the site is one way for the site to try to get more of your time so they can show you more ads. Recommender systems today are used to try to maximize user engagement or to maximize the amount of time that someone spends on a site or a specific app. Where as, the first two of these seem quite innocuous, the third, fourth, and fifth, they may be just fine. They may not cause any harm at all. Or they could also be problematic use cases for recommender systems. Let's take a deeper look at some of these potentially problematic use cases. If you are building one of these systems using recommender technology or really any other machine learning or other technology. I hope you think through not just the benefits you can create, but also the possible harm and invite diverse perspectives and discuss and debate. Please only build things and do things that you really believe can be society better off. I hope that collectively, all of us in the eye can only do work that makes people better off. Thanks for listening. We have just one more video to go in recommender systems in which we take a look at some practical tips for how to implement a content-based filtering algorithm in TensorFlow. Let's go on to that last video on recommender systems.

Subscribe to our channel for more computer science related tutorials| https://www.youtube.com/@learnwithcoursera