20. Video: Ways to Recommend: Content Based

Content Based Recommendations

In the previous notebook, you implemented collaborative filtering to make recommendations based on finding similar users. However, there were a number of users who did not receive recommendations when using this technique. For this reason, we might use other recommendation techniques to find items to recommend to users.

Another popular technique for making recommendations is called content based recommendations. In this recommendation technique, we use information that is known about the user or item to make recommendations. This method of making recommendations is particularly useful when we do not have a lot of user-item connections available in our dataset.

It might be the case that content based and collaborative filtering based techniques come up with similar recommendations, but the methods by which data scientists approach these recommendations are very different. In collaborative filtering, you are using the connections of users and items (as you did before). In content based techniques, you are using information about the users and items, but not connections (hence the usefulness when you do not have a lot of internal data already available to use).

Let's take a look at how we might implement this method with the MovieTweetings data.