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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。
By using the cloud model to represent the uncertainties in the nature such as fuzziness and randomness, this paper proposes the borrowing preference uncertainty of cloud features and readers’ multi-features. Readers’ professional, gender, and grade weighted similarity are calculated. The reader’s cloud-based similarity is calculated by using the reverse cloud algorithm to calculate the reader’s borrowing-and-returning time interval preference represented by cloud expectation, entropy and hyper-entropy. Combining readers’ multi-feature similarity and cloud similarity, readers are suggested to have duplicate books, and the validity of the algorithm is verified through experiments.