In order to improve the recommendation effect of social events in Event-based Social Network (EBSN), a personalized social event recommendation method combining historical behaviors and social relationships of users was proposed. Firstly, deep learning technology was used to build a user model from two aspects:the user's historical behaviors and the potential social relationships between users. Then, when modeling user preferences, the negative vector representation of user preferences was introduced, and the attention weight layer was used to assign different weights to different events in the user's historical behaviors and different friends in the user's social relationships according to different candidate recommendation events, at the same time, the various characteristics of events and groups were considered. Finally, a lot of experiments were carried out on the real datasets. Experimental results show that this personalized social event recommendation method is better than the comparative Deep User Modeling framework for Event Recommendation (DUMER) and DIN (Deep Interest Network) model combined with attention mechanism in terms of Hits Ratio (HR), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) evaluation indicators.

In Wireless Sensor Network (WSN), to deal with the energy limitation of nodes and the energy consumption of broadcast routing, a new WSN broadcast routing algorithm based on the improved Discrete Fruit fly Optimization Algorithm (DFOA) was proposed. Firstly, the swap and swap sequence were introduced into the Fruit fly Optimization Algorithm (FOA) to obtain DFOA, which expands the applications field of FOA. Secondly, the step of fruit fly was controlled by the Lévy flight to increase the diversity of the samples, and the position updating strategy of population was also improved by the roulette selection to avoid the local optimum. Finally,the improved DFOA was used to optimize the broadcast routing of WSN to find the broadcast path with minimum energy consumption. The simulation results show that the improved DFOA reduces the energy consumption of broadcast and has better performance than comparison algorithms including the original DFOA, Simulated Annealing Genetic Algorithm (SAGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in different network. The improved DFOA can increase the diversity of the samples, enhance the ability of escaping from local optimum and improve the network performance.

The existing dimensionality reduction methods based on manifold learning are sensitive to the selection of local neighbors, and the reduced data do not have good separability. This paper proposed a dimensionality reduction method with data separability based on adaptive neighborhood selection, which adaptively selected the neighborhood at each sample point based on estimated intrinsic dimensionality of data and local tangent orientation. Meanwhile, it clustered the similar sample points by using clustering information when mapping data, which guaranteed good separability for the reduced data and achieved better dimensionality reduction results. The experimental results show that the new method derives a better embedding result on the artificially generated data sets. In addition, it can get expected result on face visualization classification and image retrieval.