Opis
One of the most dynamically developing elds among information methods and
technologies is Big-Data (Ranjan and Foropon, 2021). The current times are characterized
by a growing in ux of new, diverse digital information, which means
that the traditional data processing mechanisms and technologies used so far are
insu cient in terms of e ciency, taking into account the processing time and the
amount of processed information (Ghasemaghaei, 2019). The information has a diverse
structure with structural, semi-structural and non-structural characteristics,
as presented in Fig. 1.1.
The increase in new data is exponential, which has an impact on the search
for new methods of analyzing available data and organizing the method of their
storage. It is necessary to search for and develop new approaches to organize
data into structures and acquire knowledge from them in the shortest possible
time. In the context of recommendation systems, Big-Data is a large collection of
unstructured data and an extensive ecosystem, which includes not only data, but
also ways of organizing data storage, data processing mechanisms and the nal
way of representing the acquired knowledge.
Discussions on determining and de ning the characteristics of Big-Data inspired
Gartner to create a model characterizing Big-Data, which is presented in
Fig. 1.2 and described in detail in (Goczyªa, 2014). This model describes the collected
and applied data that are involved in the recommendation process described
in Chapter 3 and in the pro ling mechanism presented in Chapter 4.
The use of recommendation systems is visible in every industry and area of life.
They are used as elements of larger complex logical and hardware infrastructures.
One example of their application is the collection of data on transfer and nancial
movements resulting from having bank accounts by bank customers, including the
degree of use of the banking products oered, or correlations between users and
products.
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