The application of interactive methods under swarm computing and artificial intelligence in image retrieval and personalized analysis

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The aim is to explore the interactive methods based on swarm computing and improve the image retrieval effects and personalized recommendation accuracy. The interactive methods based on swarm computing are explored. The mechanism of swarm intelligence (SI) algorithm is analyzed, in which the particle swarm optimization (PSO) algorithm and its improved algorithm are selected. The selected algorithms are combined with content-based image retrieval technology and applied to the image retrieval process, thereby realizing personalized analysis and recommendation based on users’ interests. Finally, the image retrieval behaviors of users are analyzed through simulation experiments, which verify the accuracy of the recommendation results. In the six sets of experiments, the image retrieval system based on the quantum behavior PSO (QPSO) has better performance compared to other PSO and SI evolution algorithms. The image retrieval accuracy of the proposed Bayesian personalized ranking (BPR) optimization algorithm (BPR-U2B) has significantly better performance compared to other recommendation algorithms. The QPSO algorithm is the best SI evolution algorithm for image retrieval. The BPR-U2B algorithm is combined with the collaborative filtering algorithm based on BPR. It optimizes the objective function to limit the ranking results of the BPR algorithm, which is beneficial to complete the image recommendations and improve the personalized recommendation effects for users.