Databases Reference
In-Depth Information
The
content-based approach
recommends items that are similar to items the
user preferred or queried in the past. It relies on product features and textual item
descriptions. The
collaborative approach
(or
collaborative filtering approach
) may
consider a user's social environment. It recommends items based on the opinions of
other customers who have similar tastes or preferences as the user. Recommender sys-
tems use a broad range of techniques from information retrieval, statistics, machine
learning, and data mining to search for similarities among items and customer prefer-
ences. Consider Example 13.1.
Example 13.1
Scenarios of using a recommender system.
Suppose that you visit the web site of an
online bookstore (e.g., Amazon) with the intention of purchasing a topic that you have
been wanting to read. You type in the name of the topic. This is not the first time you
have visited the web site. You have browsed through it before and even made purchases
from it last Christmas. The web store remembers your previous visits, having stored click
stream information and information regarding your past purchases. The system displays
the description and price of the topic you have just specified. It compares your interests
with other customers having similar interests and recommends additional topic titles,
saying
“Customers who bought the topic you have specified also bought these other titles as
well.”
From surveying the list, you see another title that sparks your interest and decide
to purchase that one as well.
Now suppose you go to another online store with the intention of purchasing a digital
camera. The system suggests additional items to consider based on previously mined
sequential patterns, such as
“Customers who buy this kind of digital camera are likely to
buy a particular brand of printer, memory card, or photo editing software within three
months.”
You decide to buy just the camera, without any additional items. A week later,
you receive coupons from the store regarding the additional items.
An advantage of recommender systems is that they provide
personalization
for cus-
tomers of e-commerce, promoting one-to-one marketing. Amazon, a pioneer in the use
of collaborative recommender systems, offers “a personalized store for every customer”
as part of their marketing strategy. Personalization can benefit both consumers and the
company involved. By having more accurate models of their customers, companies gain
a better understanding of customer needs. Serving these needs can result in greater suc-
cess regarding cross-selling of related products, upselling, product affinities, one-to-one
promotions, larger baskets, and customer retention.
The recommendation problem considers a set,
C
, of users and a set,
S
, of items. Let
u
be a utility function that measures the usefulness of an item,
s
, to a user,
c
. The utility is
commonly represented by a rating and is initially defined only for items previously rated
by users. For example, when joining a movie recommendation system, users are typically
asked to rate several movies. The space
C
S
of all possible users and items is huge. The
recommendation system should be able to extrapolate from known to unknown ratings
so as to predict item-user combinations. Items with the highest predicted rating/utility
for a user are recommended to that user.
