Authors :
KABEYATSHISEBA Cedric
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/41vM2Ja
Abstract :
Finding a course or book on a specific subject
in a directory can be tedious. The problem is even more
accentuated by the multidisciplinary of some of these
courses or books.
Graduate students are responsible for choosing
their study plan, the courses relevant to their field of
research, but it is not obvious that they can make the
right choice without needing to be guided or oriented.
With a tool to establish the similarity between
different documents, students could quickly find courses
or books similar to those which, for one reason or
another, are not available.
To this end, several filtering systems have been
proposed, but filtering based on content for the
recommendation of courses or books, has so far not been
addressed as done in this work, by resorting to the
measure of similarity. based on Dice's coefficient, thus
providing relatively accurate and comprehensive
recommendations. The objective of this research is to
propose a model allowing to establish the similarity
between courses and books, while being based on their
descriptions and on the calculation of their distance in a
vector space .
This reflection presents the content-based filtering
system for recommending courses and books, providing
suggestions based on their semantic similarity.
Finding a course or book on a specific subject
in a directory can be tedious. The problem is even more
accentuated by the multidisciplinary of some of these
courses or books.
Graduate students are responsible for choosing
their study plan, the courses relevant to their field of
research, but it is not obvious that they can make the
right choice without needing to be guided or oriented.
With a tool to establish the similarity between
different documents, students could quickly find courses
or books similar to those which, for one reason or
another, are not available.
To this end, several filtering systems have been
proposed, but filtering based on content for the
recommendation of courses or books, has so far not been
addressed as done in this work, by resorting to the
measure of similarity. based on Dice's coefficient, thus
providing relatively accurate and comprehensive
recommendations. The objective of this research is to
propose a model allowing to establish the similarity
between courses and books, while being based on their
descriptions and on the calculation of their distance in a
vector space .
This reflection presents the content-based filtering
system for recommending courses and books, providing
suggestions based on their semantic similarity.
