Personal Recommendation Methods Based On Weak Ties In Social Networks

In recent years,along with the emergence of various social software,the way people communicate information has gradually shifted from offline activities to online communication.As information communication methods become easier and easier,people's circle of friends continues to expand,and social networks become large and complex.It is increasingly difficult to get interesting information through social networks.Therefore,research on character recommendation algorithms based on online social networks has become crucial.It is the focus of social network research by recommending algorithms to recommend friends who can bring more fresh information to network users and make social network information more diverse.The classic character recommendation algorithm is more based on the similarity between users,recommending similar friends to the user,and does not consider the user's need for fresh and interesting heterogeneous information acquisition,thus causing certain information redundancy for the user.This paper studies the problem and proposes a weak tie recommendation algorithm in the social network.It recommends the nodes in the network that are weakly related to the user,which brings more diverse heterogeneous information to the user and promotes the information of the whole network circulation.This paper firstly expounds the definition of strong relationship and weak relationship in social network,uses community partitioning algorithm to identify strong and weak tie,and verifies the importance of weak tie to social network information circulation through classical character recommendation algorithm.On the basis of the research that weak tie can accelerate the flow of heterogeneous information on the network,the recommendation algorithm of weak relationship on online social network is proposed,and comparative experiments are carried out to verify that the recommendation algorithm based on weak tie can strengthen the information circulation of the network.The recommendation algorithm is more effective for obtaining heterogeneous information on the network.At the same time,in the research process of this subject,it is found that the community discovery algorithm has the problem of uneven community division,which has adversely affected the identification of weak tie and the importance calculation of nodes.This paper proposes a new "shortcut" based on this problem.The weak tie identification method,combined with the network topology structure,gives a new method of node importance calculation,and proposes a weak tie person recommendation algorithm based on "shortcut" and network topology in social network,which further enhances the recommendation of people based on weak tie.The heterogeneous information acquisition capability of the algorithm.