摘要
针对个性化信息服务中的用户兴趣漂移问题,提出了一种新的正态分布密度曲线遗忘函数,该函数符合用户兴趣遗忘的规律.并且将用户模型定义为长期模型和短期模型相结合的混合模型,其中短期模型使用最近最久未使用的滑动窗口算法进行更新,长期模型采用正态渐进遗忘算法进行更新.实验表明,该方法能够较迅速地发现和准确地跟踪用户的兴趣变化,提高了个性化信息服务的效率.
User interests inevitably drift while using a personalized information service. A new forgetting function with normal distribution density was proposed to accommodate drift. The function conformed to a user interest forgetting law. User interest was defined in a hybrid model that contained both long and a short-term components. The short-term component was renewed by using the least recently used algorithm. The long-term component was renewed by using the normal incremental forgetting distribution algorithm. Experiments showed that the algorithm noted changes in user＇s interests more quickly and tracked them more accurately, greatly improving the efficiency of personalized information services.
出处
《智能系统学报》
2010年第2期181-184,共4页
CAAI Transactions on Intelligent Systems
基金
陕西省科技厅自然科学基础研究计划资助项目(SJ08ZT14-8)
陕西省教育厅科学研究计划资助项目(08JK481)
咸阳师范学院专项科研基金资助项目(08XSYK335)
关键词
个性化
混合模型
兴趣漂移
遗忘函数
personalization
hybrid model
interest drift
forgetting function
