社交网络中基于强化学习的搜索(RLS)算法

Farzad Peyravi, V. Derhami, A. Latif
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引用次数: 4

摘要

社会网络分析作为一个与大众对社会网络的兴趣相重叠的学术领域,正在日益增长。寻找专家是社交网络挖掘的重要问题之一,即寻找具有相应技能和知识的合适人选。RLS算法利用Q-Learning和引荐在社交网络中寻找专家,在社交网络中搜索专家。RLS与简单搜索算法、推荐算法和SNPageRank的比较表明,RLS的准确率和召回率都有所提高。随着社会网络环境的变化,老专家的角色被新专家替代,RLS学会了寻找新的专家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement learning based search (RLS) algorithm in social networks
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.
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