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

A good way to develop a content-based filtering algorithm is to use deep learning. The approach you see in this video is the way that many important commercial state-of-the-art content-based filtering algorithms are built today. Let's take a look. Recall that in our approach, given a feature vector describing a user, such as age and gender, and country, and so on, we have to compute the vector v_u, and similarly, given a vector describing a movie such as year of release, the stars in the movie, and so on, we have to compute a vector v_m. In order to do the former, we're going to use a neural network. The first neural network will be what we'll call the user network. Here's an example of user network, that takes as input the list of features of the user, x_u, so the age, the gender, the country of the user, and so on. Then using a few layers, say dense neural network layers, it will output this vector v_u that describes the user. Notice that in this neural network, the output layer has 32 units, and so v_u is actually a list of 32 numbers. Unlike most of the neural networks that we were using earlier, the final layer is not a layer with one unit, it's a layer with 32 units. Similarly, to compute v_m for a movie, we can have a movie network as follows, that takes as input features of the movie and through a few layers of a neural network is outputting v_m, that vector that describes the movie. Finally, we'll predict the rating of this user on that movie as v_ u dot product with v_m. Notice that the user network and the movie network can hypothetically have different numbers of hidden layers and different numbers of units per hidden layer. All the output layer needs to have the same size of the same dimension. The fact that you can pre-compute ahead of time what's similar to a given movie, will turn out to be important later when we talk about scaling up this approach to a very large catalog of movies. That's how you can use deep learning to build a content-based filtering algorithm. You might remember when we were talking about decision trees and the pros and cons of decision trees versus neural networks. I mentioned that one of the benefits of neural networks is that it's easier to take multiple neural networks and put them together to make them work in console to build a larger system. What you just saw was actually an example of that, where we could take a user network and the movie network and put them together, and then take the inner product of the outputs. This ability to put two neural networks together this how we've managed to come up with a more complex architecture that turns out to be quite powerful. One notes, if you're implementing these algorithms in practice, I find that developers often end up spending a lot of time carefully designing the features needed to feed into these content-based filtering algorithms. If we end up building one of these systems commercially, it may be worth spending some time engineering good features for this application as well. In terms of these applications, one limitation that the algorithm as we've described it is it can be computationally very expensive to run if you have a large catalog of a lot of different movies you may want to recommend. In the next video, let's take a look at some of the practical issues and how you can modify this algorithm to make a scale that are working on even very large item catalogs. Let's go see that in the next video.

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