云模型和多特征的高校读者借阅偏好不确定性图书推荐研究  

Research of Books Recommendation of Borrow Preference Uncertainty in University Readers Based on Cloud Model and Multi-feature

作　　者：李克潮[1] 蓝冬梅[1] 凌霄娥[1] 

机构地区：[1]广西民族师范学院图书馆,崇左532200

出　　处：《现代图书情报技术》2013年第5期54-58,共5页New Technology of Library and Information Service

基　　金：广西教育厅科研项目"个性化推荐冷启动及稀疏性关键技术研究"(项目编号:201204LX481);CALIS广西壮族自治区文献信息服务中心预研项目"云计算环境下高校读者模糊借阅偏好的图书推荐研究"(项目编号:CALISGX201305)的研究成果之一

摘　　要：利用云模型表示自然界中模糊性、随机性等不确定性优势,提出云模型和读者多特征的借阅偏好不确定性。计算读者专业、性别、年级加权相似度,利用逆向云算法计算以云的期望、熵、超熵来表示的读者借还时间间隔偏好,再计算读者基于云的相似度。结合读者多特征相似度、云相似度,向读者推荐存在复本的图书,并通过实验验证算法的有效性。The paper, taking advantages of cloud model in expressing uncertainty of fuzziness and randomness in nature, proposes that there are uncertainties in cloud model and readers＇ multi - featured books preference. The paper computes the weighted similarity of readers＇ majors, genders and grades, uses the backward cloud algorithm to compute readers＇ preference of borrowreturn time interval that is expressed in cloud expectation, entropy and hyper entropy, and then computes the readers＇ cloud similarity. Combining multi - featured similarity of readers with cloud similarity, the paper finally recommends books to readers with copied ones and proves the validity of the algorithm by experiments.

关 键 词：云模型 读者多特征 借阅偏好不确定性 图书推荐 

分 类 号：G252[文化科学—图书馆学]