摘要
针对个性化推荐系统中用户行为分析这一重要环节,本文以用户价值和偏好习惯为切入点,通过提取用户历史记录中的隐式信息,提出了一种新的电视用户观看行为分析方法。提取用户整体平均操作频次、近期平均操作频次、操作频次变化比率、最近一次操作时间间隔作为价值模型指标,根据特征指标提出包括忠诚、新生、流失等8类不同用户价值群体分类结果;将节目划分为12类,构建基于活跃度和稳定度的用户偏好习惯特征矩阵,将用户偏好习惯分为4个簇类,对用户偏好进行量化与分析。通过对1025位用户产生的361459条播放记录数据进行实验,结果表明该方法能有效对任意电视用户观看行为进行分析,提高了偏好分析结果的准确性。
Aiming at the important part of user behavior analysis in personalized recommendation system,this paper takes user value and preference habits as the starting point.By extracting the implicit information in user history records,a new analysis method of TV user viewing behavior is proposed.The user's overall average operating frequency,the recent average operating frequency,the operating frequency change ratio,and the most recent operating time interval are extracted as the value model indicators,and the classification results of eight different user value groups including loyalty,new life,and loss are proposed according to the characteristic indicators;For 12 categories,a user preference habit feature matrix based on activity and stability is constructed,and user preference habits are divided into 4 cluster classes to quantify and analyze user preferences.Experiments on 361459 pieces of recorded data generated by 1025 users show that the method can effectively analyze the viewing behavior of any TV users and improve the accuracy of the preference analysis results.
作者
易鑫睿
陈昊
兰金明
祝纯浩
YI Xin-rui;CHEN Hao;LAN Jin-ming;ZHU Chun-hao(Key Laboratory of Nondestructive Test(Ministry of Education),Nanchang Hangkong University,Nanchang 330063,China;School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China)
出处
《南昌航空大学学报（自然科学版）》
CAS
2019年第3期96-104,110,共10页
Journal of Nanchang Hangkong University(Natural Sciences)
基金
国家自然科学基金(61772255,61866025)
江西省图像处理与模式识别重点实验室开放基金(ET201604246)
江西省自然科学基金(20181BAB202025)
关键词
用户偏好
推荐系统
电视节目
user preference
recommendation system
TV program
