Abstract
The intelligent recommendation ability of social networks is improved, a hybrid recommendation algorithm is proposed based on swarm intelligence for users' potential features in social networks. The feature extraction model of social network users is constructed, and the potential features and associated information of social network users are divided by swarm intelligence optimization technology, and the user features are learned by swarm intelligence and association rules mining. The related information of recommended items in social network is obtained, and the improvement of user item feature recommendation algorithm of social network is realized. The simulation results show that the proposed algorithm can effectively improve the accurate delivery rate of user feature recommendation in social network, and the hybrid recommendation ability for user behavior is strong, the network overhead is stable and the performance is superior
Highlights
With the popularity of the network in society, the combination of network and people's real life becomes more and more closely, people can get information in the network, education can be expanded from the network mode
In reference [5], a new feature recommendation algorithm for social network users based on multi-agent negotiation trust analysis is proposed, in which the fixed features in social network are segmented by swarm intelligence, and the recommendation accuracy is improved
In order to overcome the disadvantages of traditional methods, this paper proposes a user potential feature recommendation algorithm based on swarm intelligence in social networks
Summary
With the popularity of the network in society, the combination of network and people's real life becomes more and more closely, people can get information in the network, education can be expanded from the network mode. The user characteristics recommendation in social network is the key to ensure the security of users and the protection of privacy. Particle swarm optimization (PSO) algorithm is used to recommend the user features of social networks, and some research results have been achieved[3]. In reference [5], a new feature recommendation algorithm for social network users based on multi-agent negotiation trust analysis is proposed, in which the fixed features in social network are segmented by swarm intelligence, and the recommendation accuracy is improved. The disadvantages of traditional methods need to be solved, a user potential feature recommendation algorithm based on swarm intelligence is proposed to improve the ability of user feature recommendation and detection in social network. Simulation experiments are carried out to demonstrate the effectiveness and superiority of the proposed algorithm
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