Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 244

This chapter is organized as follows: Section 2 reviews recommender systems; Section 3
introduces academic orientation and competency based education; Section 4 shows the use
of collaborative filtering in academic orientation and points out its weaknesses; Section 5
proposes a hybrid model for academic orientation based on collaborative and content-based
techniques; Section 6 presents a Decision Support System that incorporates such a model
and Section 7 concludes this chapter.

2. Recommender systems

As we have pointed out previously the techniques that we will use to support academic
orientation will be based on those ones used in recommender systems that support
customers in their buying processes in the e-commerce arena where customers face to huge
amounts of information about items that are hard to check in an affordable time in order to
buy the most suitable item/s.
Notwithstanding, there exist several techniques in the recommender systems, in this section
we will only focus on the revision of collaborative, content-based and hybrid ones because
they will be the used in our proposal.

2.1 Collaborative recommender systems
Collaborative recommender systems (CRS) collect human opinions of items, represented as
ratings, in a given domain and group customers with similar needs, preferences, tastes, etc.,
in order to recommend active user items which liked in the past to users of the active user
group (Herlocker 1999).
Most of CRS use explicit data directly provided by users and related to customers'
perceptions and preferences that implies uncertainty, though it has been fairly usual the use
of precise scales to gather such information, the use of linguistic information to model such
an information seems more suitable and several proposals have been developed (Martínez
2007b, Porcel 2009).
There exist different collaborative approaches (Adomavicius 2005): (i) Memory-based which
use heuristics that predict ratings basing on the whole dataset of previously rated items and
(ii) Model-based which use the collection of ratings to learn a model capable of predicting
ratings. According to Figure 2, both models fulfill three general tasks to elaborate the
recommendations demanded by users:
 Analyzing and selecting data sets : data from ratings must be collected and optimized for
the system (Herlocker 2004).
 Grouping users with similar tastes and preferences in order to compute
recommendations basing on a similarity measure as Pearson Correlation Coefficient
(Adomavicius 2005, Breese 1998).
 Generating predictions : Once users have been grouped by interest (similarity), the system
uses them to compute predictions for the target customer by using different
aggregation methods (Breese 1998, Herlocker 1999).
Collaborative filtering methods provide several advantages regarding other techniques used
in recommender systems (Herlocker 1999, Sarwar 2001):
 Capability to manage information whose content is not easily analyzed automatically,

because they do not need knowledge domain, i.e., no information or knowledge about
the products is needed.