基于E-CARGO的在线社区多对多好友推荐机制研究

The Many to Many Friend Recommendation of Online Community Based E-CARGO

好友推荐机制是繁荣在线社区的有效手段,然而单纯为增加用户数及绑定用户关系的过于频繁的推荐方式会引起用户厌烦.为提升用户体验,本文以大型教学与科研协作平台学者网为研究背景,引入基于角色的协同模型ECARGO对推荐机制进行建模,将好友推荐转化为多对多指派问题,使用带回溯的Kuhn-Munkres算法(KMB)对好友推荐数与接纳数受限情况下最优推荐指派进行了研究与解决.仿真实验表明,该推荐机制友好、高效、精准,能完善在线社区推荐机制,对在线社会健康发展形成助力.

Friend recommendation is an effective method for establishing an online community.However,over frequent recommendations may be the opposite and become nuisances to users.To improve users'experience,a new method of friend recommendation is proposed via many-to-many assignment.This method limits the number of recommended and accepted friends.It takes as the application background the website http://www.scholat.com/,which is a large higher education and research collaboration platform.Recommendation is modeled via Role-Based Collaboration and its E-CARGO model.After that,the Kuhn-Munkres with Backtracking(KMB)algorithm is used to solve the optimal assignment of the proposed method.Simulation experiments show that the proposed recommendation method is friendly,efficient and accurate.It can improve the online community recommendation mechanisms,which can support the development of a virtual society.