基于粒子群算法搜索浏览设计与用户体验研究

Research on Search Browsing Design and User Experience Based on Particle Swarm Optimization Algorithm

电子商务运营过程中用户广泛使用搜索引擎并产生在线浏览行为,这些都影响用户体验并最终影响消费。文章首先根据已有成果提出研究假设,搜索引擎算法设计优化会改善用户的在线浏览行为,提升用户交互体验感受以促进消费。然后,采用粒子群算法优化了搜索引擎算法设计,并且根据在线浏览行为与用户体验相关指标体系,利用爬虫方法获取电子商务平台公司数据进行假设检验。进一步利用改进后的搜索引擎算法设计用户兴趣模型,并对用户兴趣模型进行对比试验,试验结果表明用户兴趣组优于传统算法组。得出研究结论:基于用户兴趣模型的搜索引擎算法可以有效改善用户消费体验,在线浏览与搜索引擎配合使用效果更好,既可以有效增加电子商务平台利润,也可以提升用户消费体验。

In the process of e-commerce operation,users widely use search engines and generate online browsing behavior.which affect user experience and their consumption ultimately.Based on the existing research findings.this paper puts forward the hypothesis that the optimization of search engine algorithm design will inprove the online browsing behavior of users.enhance the user interaction experience,and promote consumption.Then.particle swarm optimnixation algorithm is used to optimize the design of scarch engine algorithm.According to the index system ol online browsing behavior and user experience,the crawler tnethod is employed to obtain the company data of e commerce platform for hypothesis test.The improved search engine algorithm is used to design the user interest model,and a contrastive expcrimcnt is made with it,the results of which show that the user intcrest group is better than the traditional algorithm group.Research conclusion:the search engine algorithm bused on user interest tmodel can efectively improve the user consumption experiencer and online browsing and search engine can work better to increase effectively the profit of ecommerce plaform and improve the user consumption experience.