To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach co...
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To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.
为实现命名数据网络(NDN,name data networking)域间内容互访,提出了一种NDN域间多路径路由机制——MIRNDN。该机制使任意自治系统(AS,autonomous system)仅维护自身及客户AS可达内容的路由信息并聚合路由信息以缓解域间路由的可扩展性...
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为实现命名数据网络(NDN,name data networking)域间内容互访,提出了一种NDN域间多路径路由机制——MIRNDN。该机制使任意自治系统(AS,autonomous system)仅维护自身及客户AS可达内容的路由信息并聚合路由信息以缓解域间路由的可扩展性问题;采用"无谷底"路由策略引导请求非自身和客户AS内容的Interest报文从多路径探索内容,且请求聚合、网络缓存和自适应转发能优化探索;维护多路径路由信息以支持Interest多路径转发。从理论上分析了MIRNDN机制下FIB大小、路由更新的收敛时间和通信开销,在实际因特网AS级别拓扑上的仿真实验表明MIRNDN缓解了域间路由的可扩展性问题,路由更新的收敛时间较短,通信开销适量,并有效地减少了Interest报文的不必要转发。
为了提高推荐算法评分预测的准确度,该文在Trust Walker模型的基础上,提出了一个改进的基于信任网络和随机游走策略的评分预测模型——Referential User Walker模型。该模型通过随机游走策略,利用信任网络中的信任朋友对目标物品或与目...
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为了提高推荐算法评分预测的准确度,该文在Trust Walker模型的基础上,提出了一个改进的基于信任网络和随机游走策略的评分预测模型——Referential User Walker模型。该模型通过随机游走策略,利用信任网络中的信任朋友对目标物品或与目标物品相似的物品的评分进行评分预测,并在信任网络中找到最可信的Top N评分参考用户,同时引入信任度权重,降低了噪声数据的影响。实验结果表明,与Trust Walker模型相比,Referential User Walker模型的评分预测准确度有所提高。
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