  In recent years, huge amounts of unstructured textual data on the Internet
are a big difficulty for AI algorithms to provide the best recommendations for
users and their search queries. Since the Internet became widespread, a lot of
research has been done in the field of Natural Language Processing (NLP) and
machine learning. Almost every solution transforms documents into Vector Space
Models (VSM) in order to apply AI algorithms over them. One such approach is
based on Case-Based Reasoning (CBR). Therefore, the most important part of
those systems is to compute the similarity between numerical data points. In
2016, the new similarity TS-SS metric is proposed, which showed
state-of-the-art results in the field of textual mining for unsupervised
learning. However, no one before has investigated its performances for
supervised learning (classification task). In this work, we devised a CBR
system capable of finding the most similar documents for a given query aiming
to investigate performances of the new state-of-the-art metric, TS-SS, in
addition to the two other geometrical similarity measures --- Euclidean
distance and Cosine similarity --- that showed the best predictive results over
several benchmark corpora. The results show surprising inappropriateness of
TS-SS measure for high dimensional features.
