Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 247

where K is the total number of keywords in the system. Depending on the information and
the content based technique used, such keywords or features could be either equally
important or weighted according to their relevance, for example, using TF-IDF
(frequency/inverse document frequency) which is one of the best-known measures for
specifying keyword weights in Information Retrieval (Adomavicius 2005, Symeonidis 2007).
Although CB approach enables personalized and effective recommendations for particular
users, it has also some disadvantages (Adomavicius 2005, Lenar 2007, Symeonidis 2007):
 Limited Content Analysis: Content-based techniques are limited by the features that are
explicitly associated with the objects that these systems recommend. Therefore, in order
to have a sufficient set of features, the content must either be in a form that can be
parsed automatically by a computer (e.g., text) or the features should be assigned to
items manually. Another problem with limited content analysis is that, if two different
items are represented by the same set of features, they are indistinguishable.
 Overspecialization: When the system can only recommend items that score highly
against a user's profile, the user is limited to being recommended items that are similar
to those already rated. In certain cases, items should not be recommended if they are
too similar to something the user has already seen.
 CB is based only on the particular user relevance evaluations, but users usually are very
reluctant to give them explicit, so usually other implicit, possibly less adequate,
methods must be used.
To overcome these problems, CB and CF techniques have been combined to improve the
recommendation procedure.

2.3 Hybrid recommender systems
Due to limitations observed in both previous recommendation techniques, nowadays it
has been used to overcome these limitations the hybridization technique. This technique
combines two or more recommendations methods in order to obtain a better performance
and/or accuracy that in each of the methods separately. It is common to
combine collaborative filtering technique (CF) with another method to avoid cold start
problem.
Following it is presented the classification of hybridizing techniques for recommender
systems presented by Burke (Burke 2002a):
1. Weighted systems: recommendations of each system are combined giving each one a

specific weight of the final recommendation, depending on the system which computed
them. Importance of each item is computed based on results obtained from
recommendation techniques present in the system. All capacities of the system are used
in recommendation process in an easy and direct way, and also it is easy to adjust
weights manually or by simple algorithms. However, this technique start from the
hypothesis of giving a uniform importance value to each of the distinct techniques
composing the system, and this is not always true.

2. Switching hybrid systems use several criteria to alternate recommendation technique

whenever is needed each moment. This method brings an additional complexity in the
recommendation process as it is needed to determine choosing method criteria, as it is
another parameterization level. On the other hand, if the criterion is selected properly it
can take advantage of qualities of composing recommendation systems and avoid
weakness of systems in those situations.