Community partition probability matrix recommendation algorithm based on multiple social relations
With the continuous development of big data,the personalized recommendation of users is widely used.The existing recommendation algorithms ignore the community structure composed of various social relations between users.Relationships can be used well in recommendation systems.Based on the multi-subnet complex network model,this paper proposes a community partitioning probability matrix recommendation algorithm based on multiple social relationships by using the community structure characteristics composed of multiple social relationships.By comparing with the existing recommendation algorithms on the real data set Epinions,the accuracy evaluation indicators δMAE and δRMSE have been improved by 30%and 20%respectively.It can be proved that the community division probability matrix recommendation algorithm based on multiple social relations can effectively improve the recommended accuracy.
multi-subnet composite complex networkmulti-relational social networkcommunity structurematrix decomposition
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