摘要
提出了一种自适应的用户兴趣模型更新学习及评价方法。为了给用户提供更精准的查询结果,将用户兴趣模型加入自适应调整算法后进行验证,研究通过分析用户短期兴趣、长期兴趣规律,成为该系统建立用户的兴趣模型可能。随着时间等的变化,用户兴趣也会发生相应变化。通过自适应学习过程,为了更好地识别用户感兴趣的信息,通过研究规律进行总结分析。对兴趣学习技术进行研究,同时对该算法进行了评价。主要计算了查准率等参数,为此通过评价得出该用户兴趣挖掘精准率较好,对于现代计算机网络购物,以及网络应用过程挖掘用户行为和兴趣提供了良好的方案,也为个性化推荐应用提供了帮助。
An adaptive updating learning and evaluating method for user interest model is proposed.In order to provide users with more accurate search results,the user interest model is verified after adding adaptive adjustment algorithm.Through the analysis of user short-term interest and long-term interest in law,it becomes interested in model of users of the system.With the change of time,the user＇s interests will change accordingly.We analyze the algorithm of user＇s interest by the adaptive learning process,in which the rules change,so as to obtain the user＇s interest points.We also research on interest learning technology and evaluate it.Main parameters like precision is calculated,and the evaluation shows the user interest mining precision rate is better,providing a well solution for the modern computer network shopping and network application and process of mining user behavior and interest,with aid to personalized recommendation application.
作者
宋毅
徐志明
SONG Yi;XU Zhi-ming(Department of Computer Application and Technology,School of Electronic Information Engineering,Harbin Huade University,Harbin 150025,China;School of Computer,Harbin Institute of Technology,Harbin 150025,China)
出处
《计算机技术与发展》
2018年第6期64-66,72,共4页
Computer Technology and Development
基金
国家自然科学基金(61672185)
黑龙江省自然科学基金资助项目(F2015046)
关键词
搜索
兴趣
数据挖掘
学习
评价
search
interest
data mining
learning
evaluation
