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In this video, we'll start to develop a second type of recommender system called a content-based filtering algorithm. To get started, let's compare and contrast the collaborative filtering approach that we'll be looking at so far with this new content-based filtering approach. Let's take a look. With collaborative filtering, the general approach is that we would recommend items to you based on ratings of users who gave similar ratings as you. We have some number of users give some ratings for some items, and the algorithm figures out how to use that to recommend new items to you. In contrast, content-based filtering takes a different approach to deciding what to recommend to you. A content-based filtering algorithm will recommend items to you based on the features of users and features of the items to find a good match. In other words, it requires having some features of each user, as well as some features of each item and it uses those features to try to decide which items and users might be a good match for each other. With a content-based filtering algorithm, you still have data where users have rated some items. Well, content-based filtering will continue to use r, i, j to denote whether or not user j has rated item i and will continue to use y i, j to denote the rating that user j is given item i if it's defined. But the key to content-based filtering is that we will be able to make good use of features of the user and of the items to find better matches than potentially a pure collaborative filtering approach might be able to. Let's take a look at how this works. In the case of movie recommendations, here are some examples of features. You may know the age of the user, or you may have the gender of the user. This could be a one-hot feature similar to what you saw when we were talking about decision trees where you could have a one-hot feature with the values based on whether the user's self-identified gender is male or female or unknown, and you may know the country of the user. If there are about 200 countries in the world then also be a one-hot feature with about 200 possible values. You can also look at past behaviors of the user to construct this feature vector. For example, if you look at the top thousand movies in your catalog, you might construct a thousand features that tells you of the thousand most popular movies in the world which of these has the user watch. In fact, you can also take ratings the user might have already given in order to construct new features. It turns out that if you have a set of movies and if you know what genre each movie is in, then the average rating per genre that the user has given. Of all the romance movies that the user has rated, what was the average rating? Of all the action movies that the user has rated, what was the average rating? And so on for all the other genres. This too can be a powerful feature to describe the user. One interesting thing about this feature is that it actually depends on the ratings that the user had given. But there's nothing wrong with that. Constructing a feature vector that depends on the user's ratings is a completely fine way to develop a feature vector to describe that user. With features like these you can then come up with a feature vector x subscript u, use as a user superscript j for user j. Similarly, you can also come up with a set of features for each movie of each item, such as what was the year of the movie? What's the genre or genres of the movie of known? If there are critic reviews of the movie, you can construct one or multiple features to capture something about what the critics are saying about the movie. Or once again, you can actually take user ratings of the movie to construct a feature of, say, the average rating of this movie. This feature again depends on the ratings that users are given but again, does nothing wrong with that. You can construct a feature for a given movie that depends on the ratings that movie had received, such as the average rating of the movie. Or if you wish, you can also have average rating per country or average rating per user demographic as they want to construct other types of features of the movies as well.

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