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.

中文翻译：

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群计算和人工智能下交互方法在图像检索和个性化分析中的应用

目的是探索基于群计算的交互方法，提高图像检索效果和个性化推荐精度。探索了基于群计算的交互方法。分析了群智能(SI)算法的机理，其中选择了粒子群优化(PSO)算法及其改进算法。所选算法结合基于内容的图像检索技术，应用于图像检索过程，实现基于用户兴趣的个性化分析和推荐。最后通过仿真实验对用户的图像检索行为进行分析，验证了推荐结果的准确性。在六组实验中，基于量子行为 PSO（QPSO）的图像检索系统相比其他 PSO 和 SI 进化算法具有更好的性能。与其他推荐算法相比，所提出的贝叶斯个性化排名（BPR）优化算法（BPR-U2B）的图像检索精度具有明显更好的性能。QPSO 算法是图像检索的最佳 SI 进化算法。BPR-U2B算法与基于BPR的协同过滤算法相结合。优化目标函数，限制BPR算法的排序结果，有利于完成图像推荐，提高用户的个性化推荐效果。与其他推荐算法相比，所提出的贝叶斯个性化排名（BPR）优化算法（BPR-U2B）的图像检索精度具有明显更好的性能。QPSO 算法是图像检索的最佳 SI 进化算法。BPR-U2B算法与基于BPR的协同过滤算法相结合。优化目标函数，限制BPR算法的排序结果，有利于完成图像推荐，提高用户的个性化推荐效果。与其他推荐算法相比，所提出的贝叶斯个性化排名（BPR）优化算法（BPR-U2B）的图像检索精度具有明显更好的性能。QPSO 算法是图像检索的最佳 SI 进化算法。BPR-U2B算法与基于BPR的协同过滤算法相结合。优化目标函数，限制BPR算法的排序结果，有利于完成图像推荐，提高用户的个性化推荐效果。