en_Content-Based Recommender Systems.txt

Hello, and welcome! In this video, we’ll be covering content-based
recommendation systems. So let’s get started.
A content-based recommendation system tries to recommend items to users, based on their
profile. The user’s profile revolves around that
user’s preferences and tastes. It is shaped based on user ratings, including
the number of times that user has clicked on different items or perhaps, even liked
those items. The recommendation process is based on the
similarity between those items. Similarity, or closeness of items, is measured
based on the similarity in the content of those items.
When we say content, we’re talking about things like the item’s category, tag, genre,
and so on. For example, if we have 4 movies, and if the
user likes or rates the first 2 items, and if item 3 is similar to item 1, in terms of
their genre, the engine will also recommend item 3 to the user.
In essence, this is what content-based recommender system engines do.
Now, let’s dive into a content-based recommender system to see how it works.
Let’s assume we have a dataset of only 6 movies.
This dataset shows movies that our user has watched, and also the genre of each of the
movies. For example, “Batman versus Superman”
is in the Adventure, Super Hero genre. And “Guardians of the Galaxy” is in Comedy,
Adventure, Super Hero, and Science-Fiction genres.
Let’s say the user has watched and rated 3 movies so far and she has given a rating
of 2 out of 10 to the first movie, 10 out of 10 to the second movie, and an 8 out of
10 to the third. The task of the recommender engine is to recommend
one of the 3 candidate movies to this user. Or, in other words, we want to predict what
the user’s possible rating would be, of the 3 candidate movies if she were to watch them.
To achieve this, we have to build the user profile.
First, we create a vector to show the user’s ratings for the movies that she’s already
watched. We call it “input user ratings.” Then, we encode the movies through the
"One Hot Encoding” approach. Genre of movies are used here as a feature set.
We use the first 3 movies to make this matrix,
which represents the movie ‘feature-set’ matrix.
If we multiply these 2 matrices, we can get the “weighted feature set” for the movies.
Let’s take a look at the result. This matrix is also called the “Weighted
Genre Matrix,” and represents the interests of the user for each genre based on the movies
that she’s watched. Now, given the weighted genre matrix, we can
shape the profile of our active user. Essentially, we can aggregate the weighted
genres, and then normalize them to find the user profile.
It clearly indicates that she likes “super hero” movies more than other genres.
We use this profile to figure out what movie is proper to recommend to this user.
Recall that we also had 3 candidate movies for recommendation, that haven’t been watched
by the user. We encode these movies as well.
Now we’re in the position where we have to figure out which of them is most suited
to be recommended to the user.
To do this, we simply multiply the user-profile matrix by the candidate movie matrix, which
results in the “weighted movies” matrix. It shows the weight of each genre, with respect
to the user profile. Now, if we aggregate these weighted ratings,
we get the active user’s possible interest-level in these 3 movies.
In essence, it’s our “recommendation” list, which we can sort to rank the movies,
and recommend them to the user. For example, we can say that the “Hitchhiker's
Guide to the Galaxy” has the highest score in our list, and is proper to recommend to
the user. Now you can come back and fill the predicted
ratings for the user.
So, to recap what we’ve discussed so far, the recommendation in a content-based system,
is based on user’s tastes, and the content or feature set items.
Such a model is very efficient. However, in some cases it doesn’t work.
For example, assume that we have a movie in the “drama” genre, which the user has
never watched. So, this genre would not be in her profile.
Therefore, she’ll only get recommendations related to genres that are already in her
profile, and the recommender engine may never recommend any movie within other genres.
This problem can be solved by other types of recommender systems such as
"Collaborative Filtering.”
Thanks for watching!