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220 Selected Studies on Software and Information Systems
8.2.1 Categorization of User Characteristics
User characteristics being modeled in user model can be categorized using several different
points of view.
The way of their acquisition divides user characteristics into those which can be acquired
automatically and those which can be acquired only in cooperation with the user. An example of
such information, which can not be gathered automatically in the domain of job offers is the
list of all previous user’s employments.
Characteristics can be further divided according to their crispness. Some characteristics
are naturally fuzzy, e.g. preferred salary. Whether a salary is acceptable or good depends also
on other attributes of a job offer. Sometimes even a user can not easily express the desired
salary. However, the user is capable to express a value which is certainly unacceptable (such
a low salary can not be compensated by other attributes of a job offer) and a value of total
acceptance (this and any higher salary is certainly good). Two mentioned values present
borders of a fuzzy set. Some characteristics are on the other hand strictly crisp, e.g. a gender.
An important point of view is domain dependency of user characteristics. Domain independent
characteristics describes a user generically by attributes such as age, gender, education
etc. These characteristic could be used by multiple applications from different domains. Second
group of domain dependent user characteristics express user’s relations to the concepts of
a particular domain (research area in publication domain, preferred profession in job offers
domain etc.). Domain dependent characteristics are of little use in other domains, their usage
is however not restricted.
Finally, user characteristics can be divided into long-term and short-term. Characteristics
from the former group are more stable, i.e., their change frequency is low enough and are
used to represent user’s core features and interests. Short-term characteristics are valid only
for few sessions, sometimes they can be changed several times during one session.
8.2.2 Representation of User Characteristics
When considering user characteristics, it is important to think about their representation in
a user model. Some models are designed to store only the value of a characteristic [21]
(boolean value, string etc.), other systems are storing also a reference to a source, which
provided a characteristic [36].
Generally, it is suitable to be able to store,apart from its value, some additional information
about a characteristic (metadata). Such information could be relevance or confidence of
a characteristic [6].
Relevance of a characteristic allows to capture an importance of a characteristic to the
user. We can take preferred duty location in a domain of job offers as an example. If this
characteristic would have a high relevance, adaptive system should present them in the first
result set presented to the user. On the other hand, if a duty location has low relevance,
system could omit this attribute when searching for suitable offers.
Confidence of a characteristic is related to the way how the characteristic was acquired.
In [33] authors assign the highest confidence to characteristics provided by the user, lower
confidence is assigned to inferred characteristics. Even lower confidence can be assigned to
such a characteristic, which originates in another adaptive system and was stored in a shared
user model.