Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 248

3. Mixed hybrids result from various recommendation methods showing them at the same
time. It can be used in situations where a big number of recommendations are required.
So can be avoid the new item problem if always content-based recommendations are
shown, because this component compute its recommendations using features of new
items, regardless they have been rated or not. However, new user problem is not solved.
4. Feature combination : another way of hybridization is to manage collaborative
information as a data feature associated with each example, and to use content-based
techniques over this extended dataset. This kind of hybridization allows using
collaborative data without an exclusive dependence, decreasing system sensibility to
the number of users that have rated an item. Moreover, allow system to obtain
information about inherent similarity not possible to find in content-based systems.
5. Cascade method combines techniques by means of process composed of several phases:
preliminary selection of items to recommend by means of one of the methods. In
following phases other techniques are used to filter the set of candidates. This technique
avoids second method to manage items discarded by first method, or with enough
negative ratings to never be recommended. Thanks to this, phases next to the initial one
are more efficient. Moreover, an item rated negatively cannot be recommended because
recommendations are refined in each phase.
6. Feature augmentation : this technique produces a valuation or a classification of an item as
a new keyword or feature, and this information is incorporated in the process using
next recommendation method. This method is interesting because it allows improving
performance of main recommendation technique without altering original operation
and conception.
7. Meta - level : another way of combining several recommendation techniques is using
model generated by a method as input for another. This differs from increase of features
as this use model learned to generate features as inputs, not the whole model. The profit
of this kind of hybridization is that model apprehended is a compressed representation
of user's interests and collaborative filtering operates easier with these datasets.
As we have seen, hybridization can support the overcoming of some problems associated
with certain recommendation techniques. Although hybrid recommender systems which
use collaborative and content-based methods always will present the cold start problem as
both need a dataset to operate, once the system has a minimum dataset it can overcome
certain limitations inherent to collaboration filtering.
3. Academic background
In section 1 was introduced the aim of this chapter: to develop a DSS for academic
orientation by using a hybrid model that uses a competency based education paradigm, in
order to overcome the new subject problem. To facilitate the understanding of the
proposal this section reviews some concepts and issues related to educational systems,
academic orientation and competency based education, focusing on the Spanish Academic
System.
3.1 Educational systems
The concept of academic orientation is related to the student curriculum guidance, it means
that students have to make decisions about their curriculum in order to obtain a degree in
the topic they prefers the most or their skills are the most appropriate. So the academic