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CHAPTER 9. RECOMMENDATION SYSTEMS
9.2
Content-Based Recommendations
As we mentioned at the beginning of the chapter, there are two basic architec-
tures for a recommendation system:
1. Content-Based systems focus on properties of items. Similarity of items
is determined by measuring the similarity in their properties.
2. Collaborative-Filtering systems focus on the relationship between users
and items.
Similarity of items is determined by the similarity of the
ratings of those items by the users who have rated both items.
In this section, we focus on content-based recommendation systems. The next
section will cover collaborative ﬁltering.
9.2.1
Item Proﬁles
In a content-based system, we must construct for each item a proﬁle, which is
a record or collection of records representing important characteristics of that
item. In simple cases, the proﬁle consists of some characteristics of the item
that are easily discovered. For example, consider the features of a movie that
might be relevant to a recommendation system.
1. The set of actors of the movie. Some viewers prefer movies with their
favorite actors.
2. The director.
Some viewers have a preference for the work of certain
directors.
3. The year in which the movie was made. Some viewers prefer old movies;
others watch only the latest releases.
4. The genre or general type of movie. Some viewers like only comedies,
others dramas or romances.
There are many other features of movies that could be used as well. Except
for the last, genre, the information is readily available from descriptions of
movies. Genre is a vaguer concept. However, movie reviews generally assign
a genre from a set of commonly used terms. For example the Internet Movie
Database (IMDB) assigns a genre or genres to every movie.
We shall discuss
mechanical construction of genres in Section 9.3.3.
Many other classes of items also allow us to obtain features from available
data, even if that data must at some point be entered by hand. For instance,
products often have descriptions written by the manufacturer, giving features
relevant to that class of product (e.g., the screen size and cabinet color for a
TV). Books have descriptions similar to those for movies, so we can obtain
features such as author, year of publication, and genre. Music products such
as CD’s and MP3 downloads have available features such as artist, composer,
and genre.
