Information Technology Reference
In-Depth Information
increase of requirements in many areas. One of the most demanded solutions is based on the
necessity of finding out suitable items over huge and/or complex search spaces in e-shops .
The users need help to explore and filter all the possibilities about the items offered in order
to improve the quality of their choices, minimize the time consumed and the wrong
decisions.
Different tools have been developed to accomplish the previous goals, being remarkable the
use of Recommender Systems (Adomavicius 2005, Resnick 1997, Rodríguez 2010). These
systems offer recommendations to users according to their preference profiles, guiding them
through search spaces in order to find out the most suitable items for their needs in many
real-life situations. The growth of this area is basically due to the vast amount of available
information in Internet and the facilities provided by the Web to create users' communities
in order to share experiences, tastes, interests, etc. In this sense, Recommender Systems (RS)
provide users customized advices and information about products or services of interest in
order to support their decision-making processes. Usually a RS estimates the degree of like
or dislike either how suitable is an item for a user. To do so, a user profile is generated from
his/her preferences which are built by gathering explicit data, implicit data or both
(Adomavicius 2005, Pazzani 1999) (see Figure 1).
Fig. 1. Recommendation scheme
Recommendations suggested by RS can be obtained in different ways (Resnick 1997, Schafer
2001) and hence there exist different types of RS, depending on the information and the
technique utilized to compute its recommendations:
Content-based Recommender System (Horvath 2009, Martínez 2007b): These RS are based
on features of items experienced by the customer in order to create users' profiles and
use these to find out similar items.
Collaborative Filtering Recommender Systems (Goldberg 1992, Takacs 2009): Instead of
building users' profiles based on item features, they use customers' ratings to compute a
recommendation of items for a specific user considering the similarity of the target
customer and the rest of users.
Knowledge Based Recommender Systems (Burke 2000, Zhen 2010): These systems base their
recommendations on inferences about user's needs and preferences. They use the
knowledge about customers' necessities and how an item matches these necessities to
infer useful recommendations.
Demographic Recommender Systems (Krulwich 1997): They categorize customers into
groups using stereotype reasoning based on the information stored in the user profile
that contains different demographic features.
Search WWH ::




Custom Search