Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommend...近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommendation algorithm based on the CosRA,RTECosRA)。该算法在“用户-物品”的二部图网络中,基于CosRA相似性指标进行资源分配,在资源分配过程中引入用户的信任关系,调整受信任用户获得的资源值,从而提高受信任用户所选物品的推荐率。在FriendFeed和Epinions数据集上的实验结果显示,相比于基准算法,RTECosRA算法在准确性和多样性上均有提高,且加入信任关系后,扩大了用户的可推荐范围,一定程度上缓解了冷启动问题。展开更多
社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现...社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现有研究通常基于个体间的观点距离来更新社交网络结构,没有考虑个体间的信任关系对网络结构的影响.为了应对上述挑战,提出一种社交网络中动态信任感知的观点演化模型(Dynamic Trust-Aware Opinion Evolution Model in Social Networks,DTAOE).具体地,首先基于信任传播规则构建出社交群体的信任矩阵;之后,基于引入的信任度阈值和信任矩阵,从邻居节点以及非邻居节点中确定当前个体的信任集合,进而基于信任集合中观点相似的个体更新当前个体的观点;最后,根据个体间的观点距离和信任关系,动态地调整社交网络的拓扑结构.上述演化步骤被重复执行直到群体的观点达到稳定状态.开展了大量的仿真实验,实验结果证明了DTAOE模型的有效性和合理性,并揭示了网络结构和信任关系对观点传播的影响机制.展开更多
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
文摘近年来,社会化推荐成为了推荐领域的研究热点。在基于用户历史行为的推荐算法中引入用户的社交关系,能够缓解推荐系统面临的数据稀疏性和冷启动的问题。本文提出了一种基于相对信任增强的推荐算法(relative trust enhancement recommendation algorithm based on the CosRA,RTECosRA)。该算法在“用户-物品”的二部图网络中,基于CosRA相似性指标进行资源分配,在资源分配过程中引入用户的信任关系,调整受信任用户获得的资源值,从而提高受信任用户所选物品的推荐率。在FriendFeed和Epinions数据集上的实验结果显示,相比于基准算法,RTECosRA算法在准确性和多样性上均有提高,且加入信任关系后,扩大了用户的可推荐范围,一定程度上缓解了冷启动问题。
文摘社交关系网络的复杂性和动态性为观点演化研究带来三大挑战:一是研究者在确定个体的观点交互集合时没有考虑个体的信任阈值,导致观点交互集合的准确性不足;二是现有研究通常忽略了非邻居节点之间的交互对社会群体观点演化的影响;三是现有研究通常基于个体间的观点距离来更新社交网络结构,没有考虑个体间的信任关系对网络结构的影响.为了应对上述挑战,提出一种社交网络中动态信任感知的观点演化模型(Dynamic Trust-Aware Opinion Evolution Model in Social Networks,DTAOE).具体地,首先基于信任传播规则构建出社交群体的信任矩阵;之后,基于引入的信任度阈值和信任矩阵,从邻居节点以及非邻居节点中确定当前个体的信任集合,进而基于信任集合中观点相似的个体更新当前个体的观点;最后,根据个体间的观点距离和信任关系,动态地调整社交网络的拓扑结构.上述演化步骤被重复执行直到群体的观点达到稳定状态.开展了大量的仿真实验,实验结果证明了DTAOE模型的有效性和合理性,并揭示了网络结构和信任关系对观点传播的影响机制.
