Welcome back. While we know that some of you will be continuing on to the Capstone course and project, we also know that the majority of you are probably finishing this course in specialization here. And so we wanted to take just a few minutes to wrap things together and hopefully give you a few words as you head out to practice and use what you've learned about recommender systems. >> We hope that through this course, you've come to have a much better understanding of how recommender systems are used and deployed in practice. Both, how they're integrated into business processes and user experiences. How users interact with them, make use of the recommendations. Provide input that's going to improve or harm their future recommendations. How to build these systems. And also how to understand whether or not the thing that you've built Is producing value for your users and for the business that's operating it. And also, do you recognize a lot of the options and complexity in actually designing and building out these systems? From how you select the algorithms, how you select the evaluation strategy, where you present the recommendations, how you do that, the data you make use of. There's a lot of complex decisions that you're going to need to make. We hope that we've been able to equip you better to understand these decisions and start to make some informed ones. >> So as you go forward, there's lots of places to get more information. There are plenty of books if you go to your favorite book store or online, or physical. There are wonderful collections of reference material on recommender systems that go in greater depth into the topics that we've introduced in this course. But this is also a very fast moving field. And if you want to keep on top of the latest that's going on, you're going to have to follow research as it emerges primarily in this field, in the conferences. The ACM Recommender Systems Conference, which we've mentioned before, and which we regularly attend, and would be delighted to meet you in some future year. But the international conferences on data mining, and on machine learning, the Knowledge Discovery in Databases conference and the SIGIR conference and CIKM conferences focusing on information retrieval and information and knowledge management. Among many others are places where, each year, new ideas in recommender systems are presented, published with algorithms, with data, with evaluations. And it's just a valuable place to keep up in the field. So we do hope you will find recommender systems to be something you want to apply. Use that knowledge maybe in some new and interesting ways. Or just appreciate and start to understand better what's going on when you visit the kinds of sites and services that apply them themselves. Whatever you do, tell us, share your learner stories with us online. It's a great thing for us to do to sit down and browse what people have gotten out of the course, how that's been useful. But most of all, we just want to spend a moment to thank you. We've enjoyed the opportunity to reach out to you whether it's through your living room TV, or your cell phone or any device in between. And we appreciate the trust that you have put in us to introduce you to this topic that we care so much about. Happy recommending.
