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[1]廖国琼*,黄志伟.基于事件社会网络中考虑约束全局推荐策略[J].南京大学学报(自然科学),2018,54(1):11.[doi:10.13232/j.cnki.jnju.2018.01.002]
 Liao Guoqiong*,Huang Zhiwei.A global recommendation strategy considering constraints in event-based social networks[J].Journal of Nanjing University(Natural Sciences),2018,54(1):11.[doi:10.13232/j.cnki.jnju.2018.01.002]
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基于事件社会网络中考虑约束全局推荐策略()
     

《南京大学学报(自然科学)》[ISSN:0469-5097/CN:32-1169/N]

卷:
54
期数:
2018年第1期
页码:
11
栏目:
出版日期:
2018-02-01

文章信息/Info

Title:
A global recommendation strategy considering constraints in event-based social networks
作者:
廖国琼12*黄志伟1
1.江西财经大学信息管理学院,南昌,330013;2.江西省高校数据与知识工程重点实验室,南昌,330013
Author(s):
Liao Guoqiong12*Huang Zhiwei1
1.School of Information Technology,Jiangxi University of Finance and Economics,Nanchang,330013,China;
2.Jiangxi Province Key Laboratory of Data and Knowledge Engineering,Nanchang,330013,China
关键词:
基于事件社会网络全局事件推荐二进制粒子群优化约束优化
Keywords:
event-based social networksglobal event recommendationbinary particle swarm optimizationconstrained optimization
分类号:
TP181
DOI:
10.13232/j.cnki.jnju.2018.01.002
文献标志码:
A
摘要:
近年来,以Meetup,Plancast和Douban为代表的基于事件社会网络(Event-based Social Networks,EBSN)得到快速发展,其推荐策略得到越来越多关注.EBSN推荐系统应同时考虑用户和事件组织者的需要,即在尽可能满足用户偏好兴趣的同时,要保证事件资源的全局均衡分配,因此研究EBSN全局推荐策略十分迫切且必要.然而,由于EBSN存在多种约束条件,包括用户允许参与事件数、事件允许接纳用户数和时间冲突等约束,仅面向用户的传统推荐方法已不再适用.首先定义了EBSN全局推荐优化目标,即在满足约束条件下,使得全部用户的不满意度和全部事件的不满意度之和最小化;然后,为将约束问题转化为非约束问题求解,分别为三类约束条件建立惩罚项,并生成考虑约束的单一目标函数.为有效实现优化目标,提出了考虑约束的二进制粒子群优化算法(Constrained Binary Particle Swarm Optimization,CBPSO),其优点是能够解决多约束条件下二进制离散空间问题.为进一步提高优化性能,分别提出了一种增进的二进制粒子群优化算法(Improved Binary Particle Swarm Optimization,IBPSO)和支持全局推荐扩展的二进制粒子群优化算法(Extended Improved Binary Particle Swarm Optimization,EX-IBPSO).在真实数据集上进行了性能测试,结果验证了所提出方法可行且有效.
Abstract:
In recent years,with the rapid development of event-based social networks(EBSN),such as Meetup,Plancast and Douban,which offer platforms for users to plan arrangement and publish events,and the events recommendation strategies have gained more and more attentions.EBSN recommendation systems should take into account the requirements of the users and event organizers at the same time.That is,it should not only meet the users’ preference as far as possible,but also ensure global balanced allocation of event resources,i.e.participants are arranged with personally interesting events and event organizers tend to enroll more participants.Thus,it is very urgent and necessary to study EBSN global recommended strategies.However,due to the multiple constraints in EBSN,including the number of events that users are allowed to participate in,the number of users that events allow to accept and the conflict constraints between events and so on,the user-oriented traditional recommendation methods are no longer applicable for EBSN global recommendation.In the paper,we focus on making event-participant arrangements in a global view.We first define the optimization object of global recommendation,that is,to minimize the sum of all users’ dissatisfaction and all events’ dissatisfaction satisfying constraint conditions.Then,in order to transform the constrained problem into a non-constrained problem,we establish a penalty term for each kind of constraints,and get a single objective function.For achieving the optimization object,we suggest a constrained binary particle swarm optimization algorithm(CBPSO),the advantage of which is that it can solve the binary discrete space problem under multiple constraints conditions.For improving the optimization performance further,an improved binary particle swarm optimization(IBPSO)algorithm and an extended IBPSO algorithm(EX-IBPSO)for global recommendation are proposed.The experiments on a real dataset show that the proposed approaches are available and effective.

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相似文献/References:

备注/Memo

备注/Memo:
基金项目:国家自然科学基金(61772245,61262009),江西省自然科学基金(20151122040083),江西省教育厅重点科技项目(GJJ160419),江西省优势科技创新团队建设计划(20113BCB24008)
收稿日期:2017-12-19
*通讯联系人,E-mail:liaoguoqiong@163.com
更新日期/Last Update: 2018-01-30