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What are Binary Labels in Collaborative Filtering Algorithms? How Can You Give Ratings? Let's take a look. Many important applications of recommended systems or collective filtering algorithms involved binary labels where instead of a user giving you a one to five star or zero to five star rating, they just somehow give you a sense of they like this item or they did not like this item. Let's take a look at how to generalize the algorithm you've seen to this setting. The process we'll use to generalize the algorithm will be very much reminiscent to how we have gone from linear regression to logistic regression, to predicting numbers to predicting a binary label back in course one, let's take a look. Here's an example of a collaborative filtering data set with binary labels. A one the notes that the user liked or engaged with a particular movie. So label one could mean that Alice watched the movie Love at last all the way to the end and watch romance forever all the way to the end. But after playing a few minutes of nonstop car chases decided to stop the video and move on. Or it could mean that she explicitly hit like or favorite on an app to indicate that she liked these movies. But after checking out nonstop cloud chasers and swords versus karate did not hit like. And the question mark usually means the user has not yet seen the item and so they weren't in a position to decide whether or not to hit like or favorite on that particular item. So the question is how can we take the collaborative filtering average that you saw in the last video and get it to work on this dataset. And by predicting how likely Alice, Bob carol and Dave are to like the items that they have not yet rated, we can then decide how much we should recommend these items to them. There are many ways of defining what is the label one and what is the label zero, and what is the label question mark in collaborative filtering with binary labels. Let's take a look at a few examples. In an online shopping website, the label could denote whether or not music they chose to purchase an item after they were exposed to it, after they were shown the item. So one would denote that they purchase it zero would denote that they did not purchase it. And the question mark would denote that they were not even shown were not even exposed to the item. Or in a social media setting, the labels one or zero could denote did the user favorite or like an item after they were shown it. And question mark would be if they have not yet been shown the item or many sites instead of asking for explicit user rating will use the user behavior to try to guess if the user like the item. So for example, you can measure if a user spends at least 30 seconds of an item. And if they did, then assign that a label one because the user found the item engaging or if a user was shown an item but did not spend at least 30 seconds with it, then a sign that a label zero. Or if the user was not shown the item yet, then assign it a question mark. Another way to generate a rating implicitly as a function of the user behavior will be to see that the user click on an item. This is often done in online advertising where if the user has been shown an ad, if they clicked on it assigned to the labor one, if they did not click assigned to the label zero and the question mark were referred to if the user has not even been shown that ad in the first place. So often these binary labels will have a rough meaningless follows. A labor of one means that the user engaged after being shown an item And engaged could mean that they clicked or spend 30 seconds or explicitly favorite or like to purchase the item. A zero will reflect the user not engaging after being shown the item, the question mark will reflect the item not yet having been shown to the user. As we plug this into here, then this gives you the cost function they could use for collaborative filtering on binary labels. So that's it. That's how you can take the linear regression, like collaborative filtering algorithm and generalize it to work with binary labels. And this actually very significantly opens up the set of applications you can address with this algorithm. Now, even though you've seen the key structure and cost function of the algorithm, there are also some implementation, all tips that will make your algorithm work much better. Let's go on to the next video to take a look at some details of how you implement it and some little modifications that make the album run much faster. Let's go on to the next video.

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