  In the last decade we have observed a mass increase of information, in
particular information that is shared through smartphones. Consequently, the
amount of information that is available does not allow the average user to be
aware of all his options. In this context, recommender systems use a number of
techniques to help a user find the desired product. Hence, nowadays recommender
systems play an important role. Recommender Systems' aim to identify products
that best fits user preferences. These techniques are advantageous to both
users and vendors, as it enables the user to rapidly find what he needs and the
vendors to promote their products and sales. As the industry became aware of
the gains that could be accomplished by using these algorithms, also a very
interesting problem for many researchers, recommender systems became a very
active area since the mid 90's. Having in mind that this is an ongoing problem
the present thesis intends to observe the value of using a recommender
algorithm to find users likes by observing her domain preferences. In a
balanced probabilistic method, this thesis will show how news topics can be
used to recommend news articles. In this thesis, we used different machine
learning methods to determine the user ratings for an article. To tackle this
problem, supervised learning methods such as linear regression, Naive Bayes and
logistic regression are used. All the aforementioned models have a different
nature which has an impact on the solution of the given problem. Furthermore,
number of experiments are presented and discussed to identify the feature set
that fits best to the problem.
