چکیده مقاله
Friend recommendation is a significant issue in social networks, aimed at suggesting new friendships to users This research proposes a novel approach for optimizing friend recommendations in social networks based on the network's graph structure, utilizing an improved K Means clustering method enhanced by cuckoo search A structure aware recommendation system was employed in this study, meaning that within each cluster, the most similar individuals to the current user were identified based on graph structural features such as the number of mutual friends This method effectively selects individuals who share structural similarities, thereby improving recommendation accuracy To achieve this, the Jaccard coefficient was used The effectiveness of the proposed method was evaluated using social network datasets, specifically Facebook data provided by Stanford University The results indicate that the proposed method demonstrates superior performance compared to baseline algorithms and previous studies
کلیدواژهها
نویسندگان
Elaheh Nooraddini
Vocational Instructor, Department of Education, Chatrood, Kerman, Iran
شیوه ارجاع
Nooraddini, Elaheh,1403,Friend Recommendation in Social Networks Using Clustering and Evolutionary Optimization,The 5th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات17 اسفند 1403 · مشهد