基于兴趣衰减的个性化排序算法

Personalization Sorting Algorithm Based on Interest Attenuation

目前多数个性化排序算法未考虑用户兴趣随时间产生的漂移变化,从而影响排序质量。为此,提出一种融合用户兴趣衰减的个性化排序算法。利用传统个性化排序算法的用户兴趣模型,及用户搜索兴趣的变化规律,分析搜索兴趣程度的时间衰减性,以人类遗忘曲线为基础给出适合搜索兴趣变化的指数遗忘函数,并将其运用到传统个性化排序算法中。实验结果表明,与基于兴趣模型的个性化排序算法相比,该算法能提高个性化搜索引擎的查准率。

The most currentranking algorithm don’t take users’ interest drifting over time into consideration,which affects the sorting quality. In order to solve this problem,a method ranking algorithm merging user interest attenuation is proposed. In this method,users’ interest model of traditional personalization ranking algorithm and changing law of users’ searching interests are used to analyze the time attenuation of search interest. The exponential forgetting function fitting searching interest based on human forgetting curve is proposed and applied to the personalized ranking algorithm.Experimental results show that the new algorithm can improve the precision of personalized search engine compared with the ranking algorithm based on users’ interest model.