Recommendation Systems - Mining of Massive Datasets - page 296

9.1.4 Populating the Utility Matrix

Without a utility matrix, it is almost impossible to recommend items. However, acquiring
data from which to build a utility matrix is often difficult. There are two general approaches
to discovering the value users place on items.

(1) We can ask users to rate items. Movie ratings are generally obtained this way, and
some on-line stores try to obtain ratings from their purchasers. Sites providing content,
such as some news sites or YouTube also ask users to rate items. This approach is lim-
ited in its effectiveness, since generally users are unwilling to provide responses, and
the information from those who do may be biased by the very fact that it comes from
people willing to provide ratings.
(2) We can make inferences from users' behavior. Most obviously, if a user buys a product
at Amazon, watches a movie on YouTube, or reads a news article, then the user can be
said to “like” this item. Note that this sort of rating system really has only one value:
1 means that the user likes the item. Often, we find a utility matrix with this kind of
data shown with 0s rather than blanks where the user has not purchased or viewed the
item. However, in this case 0 is not a lower rating than 1; it is no rating at all. More
generally, one can infer interest from behavior other than purchasing. For example, if
an Amazon customer views information about an item, we can infer that they are in-
terested in the item, even if they don't buy it.

9.2 Content-Based Recommendations
As we mentioned at the beginning of the chapter, there are two basic architectures 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.