Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 256

choose in Baccalaureate, after finishing Secondary School. Specifically, the system aided
advisors to obtain useful information about which subjects in each modality and which
elective subjects suited better a student or which core subject might be extra hard. Thanks to
this system advisors can develop their duties quicker and with reliable information.
However as it was pointed out, this system presented overall the new item problem which
makes impossible to offer complete recommendations because in a continuous changing
system, new subjects appears almost every year and CF is no able to support this kind of
information. To solve this limitation and those seen in previous sections a new Hybrid-
OrieB system has been built, using both CF and CB approaches in order to provide better
recommendations and to overcome CF inherent limitations.

Fig. 3. Home Page of OrieB
Due to the importance that the information provided by this system can perform in the
future decisions of students in early ages that they are not mature, we decided that it will be
used just for advisors in order to support students but not directly by the students due to
their lack of maturity.
The next step consists of choosing a hybridization method and presents the new system.

6.1 Hybridizing CF and CB for academic orientation
The main purpose of this chapter is to avoid the new item problem in OrieB by hybridizing
CF and CB. When a new subject is presented in the system, it has no marks from students as
it hasn't been studied yet by anybody. CF is unable to recommend this kind of subjects. This
fact points out to the use of CB, as CB will always provide a recommendation for every
target subject so that new subjects will not be a problem.
At this point, the system would obtain 2 lists of recommendations, one from CB and one
from CF. Provided that CF uses 15 top subjects to elaborate its recommendation, we will set
N for CB also in 15. So, we will have 2 lists of 15 subjects each with which we are going to
work the hybridization.
In this sense, system will use subjects in both lists as follows:
 If dataset has no marks for a selected subject, CB recommendation will be used and the

subject will be used to build the recommendation, because it is a new subject .

 If dataset presents any marks for that subject, system will weight recommendation of

CF and CB using its own computed CF trust (Castellano 2009a, Castellano 2009b). Let