Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words
DOI:
https://doi.org/10.3991/ijet.v17i12.30375Keywords:
Machine Learning, Higher Education, Cosine Similarity, TF-IDFAbstract
Comparing textual content is becoming more and more problematic due to the fact that nowadays data is very dynamic. The application of sophisticated methods enables us to compare how similar the documents are to each other. In our research we apply the Cosine Similarity method to compare the similarity of several documents with each other. We also apply the TF-IDF technique which enables us to normalize the results. Normalization of these results is necessary for the fact that there are some words that are repeated several times and from this repetition is determined their importance. Finally we can see a comparison between the similarity of documents with normalized and non-normalized results. As can be seen in the research, the normalization of results has a great value in comparing documents with textual content.
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Copyright (c) 2022 Ylber Januzaj, Artan Luma
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