基于分页缓存模型的用户兴趣跟踪方法

对智能推荐系统中用户兴趣跟踪问题的研究,传统方法如时间窗口、遗忘函数等在表征用户兴趣模型时均未考虑兴趣主题概念相关性,无法充分利用用户历史数据,导致兴趣跟踪不准确。因此,本文提出了基于分页缓存的用户兴趣表征模型,形成基于主题的用户多兴趣域结构,并提出了相应的兴趣迁移检测SIM算法,该算法引入序列熵差,表征兴趣迁移的整体特性。实验表明,与传统方法相比,本文提出的方法具有更低的兴趣平均绝对偏差,能够更准确地表征用户兴趣迁移,从而获得更好的推荐质量和效率。

User Interest Tracking Method Based on Paging Cache Model

Aiming at tracking user's interest in intelligent recommender systems,traditional methods such as Time Window,Forgotten Function,consider little in topic relevance when characterizing user's interest model and could hardly make full use of historic data,leading to inaccurate in tracking user's interest migration.Therefore,the paper proposes a new characteristic model based on paging cache,which forms user's multiple interest domains classified by topics.This paper proposes the corresponding SIM algorithm which introduces sequence entropy difference to characterize the integral features of interest migration.Experimental results show that the proposed method provides lower mean absolute error of interest and precisely characterize user's interest migration,so as to enhance the quality and efficiency in services.