Graph-Based Recommendation System Enhanced with Community Detection

• URL: http://arxiv.org/abs/2201.03622v3
• Date: Thu, 27 Jul 2023 08:19:29 GMT
• Title: Graph-Based Recommendation System Enhanced with Community Detection
• Authors: Zeinab Shokrzadeh, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Jamshid Bagherzadeh-Mohasefi
• Abstract summary: 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. This article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution.
• Score: 7.436429318051602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, using thesaurus 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 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 has considered the time of tag assignments in co-occurrence tags for determining similarity of tags. Then the graph is created based on similarity of tags. 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 evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7% respectively.

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