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基于遗忘曲线的协同过滤推荐算法 预览 被引量:58

A collaborative filtering recommendation algorithm based on forgetting curve
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摘要 协同过滤是成功的个性化推荐技术之一.但传统协同过滤算法由于不能及时反映用户的兴趣变化,影响了推荐质量.针对这个问题,本文借鉴心理学上艾宾浩斯遗忘曲线来跟踪和学习用户的兴趣,展开了协同过滤推荐算法的研究.通过数学分析工具发现了与遗忘曲线拟合度较高的幂函数曲线,并把用户的兴趣分为短期兴趣和长期兴趣,提出了基于时间窗口的权重函数,以此解决跟踪和学习用户兴趣的难题.结合项目的评分相似性和属性相似性来定义项目相似度数据权重函数.将基于时间窗的数据权重与基于项目相似度的数据权重相结合来反应用户对项目的兴趣度.最后,在项目近邻模型的基础上设计了跟踪用户兴趣变化的基于遗忘曲线的协同过滤推荐算法.通过大量的实验工作确定了相关公式中系数的取值;对比实验结果表明新的协同过滤推荐算法在推荐的准确性方面有显著的提高. Recommender systems analyze patterns of user interest in items or products to provide personalized recommendations for items that are likely to be of interest to the user.Recommender systems are very important for e-commerce.One of the most promising recommendation technologies is collaborative filtering.However,the existing collaborative filtering methods do not consider the drifting of the user's interests.For this reason,the systems may recommend unsatisfactory items when the user's interests changed.In order to produce high quality of recommendation,a novel collaborative filtering recommendation algorithm is proposed in this paper,which can study and trace the user's interests through studying Ebbinghaus forgetting curve.In order to adapt and trace the drifting of user's interests,this paper proposes a collaborative filtering method based on the forgetting curve.Thus,we divide the user's interests into the long-term interests and the short-term interests.For the short-term interest,because human memory is limited,if the item cannot be revisited very soon,it will soon be forgotten.For the long-term interests,if the item is not revisited for a long time for some reason such as the changeable environment,the item will be gradually forgotten.The short term interests are changeable with the special items such as electrical products,or the special time such as seasons.Otherwise,the long-term interests are stable.Therefore,we divide a period of visiting time into several chronological sub-time periods,each sub-time period called a time-window.The interval between the two time-windows is different.The speed of forgetting for the items in the time window is supposed to be the same.It is reasonable that we suppose the change curve of the size of time-windows is similar to the forgetting curve.That is,the time-window is more narrow if it more near to "now"(the current time),and the time-window is more broad if it more far away from "now"(the current time).Furthermore,we find a special power fun
作者 于洪 李转运 Yu Hong,Li Zhuan-Yun (Institute of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing,400065,China)
出处 《南京大学学报:自然科学版》 CAS CSCD 北大核心 2010年第5期520-527,共8页 Journal of Nanjing University: Nat Sci Ed
基金 重庆市科委项目(2009BB2082) 重庆市教委项目(KJ080510)
关键词 协同过滤 个性化推荐 兴趣变化 基于时间窗口的数据权重 collaborative filtering personalized recommendation interest drift weight based on time-window
作者简介 通讯联系人,Email:yuhong@cqupt.edu.cn
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参考文献15

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