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基于特定信息需求的网站用户游历其兴趣文档集合的便利性,建立了一种站点结构优化的数学模型,通过页组支持度与页组拓扑平均距离量化评估与挖掘站点中访问效率较低的内容文档集合为结构优化的兴趣页组,据此提出能综合评价站点访问效率的指标——WEB拓扑兴趣度,并通过分析新增超链接的影响因素设计了相应的站点结构优化方法,优化算法中采用遗传算法寻找最优组合的新增超链接组。实验结果表明:优化后的站点结构能有效改善信息搜索与获取行为的效率低下问题。
Based on the convenience of website users who travel through their interest documents collection based on specific information requirements, a mathematical model of site structure optimization is established. Through page group support and page group topology, the average distance is used to quantitatively evaluate and mine the less efficient access to the site The set of documents is a set of interest-optimized pages, and the index of WEB topological interest can be comprehensively evaluated. The corresponding site structure optimization method is designed by analyzing the influencing factors of new hyperlinks. In the optimization algorithm Genetic algorithm to find the optimal combination of the new hyperlink group. The experimental results show that the optimized site structure can effectively improve the efficiency of information search and retrieval.