Title :
Reinforcement learning based search (RLS) algorithm in social networks
Author :
Peyravi, Farzad ; Derhami, Vali ; Latif, Alimohammad
Author_Institution :
Electr. & Comput. Eng. Dept., Yazd Univ., Yazd, Iran
Abstract :
Social network analysis has an increasing growth as an academic field which overlaps with popular interest in social networks. Search for an expert is one of the most important issues of mining of social networks which is finding the right person with the suitable skills and knowledge. The RLS algorithm exploited Q-Learning and referrals to find experts in social network to search expert in social network. Comparison of RLS with Simple Search Algorithm, Referral Algorithm and SNPageRank shows increase in both precision and recall. RLS learns to find new experts as old experts substitute their role with new ones due to changes in social network environment.
Keywords :
data mining; learning (artificial intelligence); search problems; social networking (online); Q-learning; RLS algorithm; SNPageRank; data mining; referral algorithm; reinforcement learning based search; simple search algorithm; social networks; Artificial intelligence; Bismuth; DH-HEMTs; Hafnium; Hidden Markov models; Find Expert; Q-Learning; Reinforcement Learning; Social Network;
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on
Conference_Location :
Mashhad
Print_ISBN :
978-1-4799-8817-4
DOI :
10.1109/AISP.2015.7123527
