Resource recommender system performance improvement by exploring similar tags and detecting tags communities. (arXiv:2201.03622v1 [cs.IR])

Many researchers have used tag information to improve the performance of
recommendation techniques in recommender systems. Examining the tags of users
will help to get their interests and leads to more accuracy in the
recommendations. Since user-defined tags are chosen freely and without any
restrictions, problems arise in determining their exact meaning and the
similarity of tags. On the other hand, using thesauruses and ontologies to find
the meaning of tags is not very efficient due to their free definition by users
and the use of different languages in many data sets. Therefore, this article
uses the mathematical and statistical methods to determine lexical similarity
and co-occurrence tags solution to assign semantic similarity. On the other
hand, due to the change of users’ interests over time this article have
considered the time of tag assignments in co-occurrence tags for determined
similarity of tags. Then the graph is created based on these similarities. For
modeling the interests of the users, the communities of tags are determined by
using community detection methods. So recommendations based on the communities
of tags and similarity between resources are done. The performance of the
proposed method has been done using two criteria of precision and recall based
on evaluations with “Delicious” dataset. The evaluation results show that, the
precision and recall of the proposed method have significantly improved,
compared to the other methods.



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