计算机科学 ›› 2015, Vol. 42 ›› Issue (7): 254-257.doi: 10.11896/j.issn.1002-137X.2015.07.054
张玮奇,张宏志,左旺孟,崔梦天
ZHANG Wei-qi, ZHANG Hong-zhi, ZUO Wang-meng and CUI Meng-tian
摘要： 协同过滤是目前推荐系统最常用的技术之一,相比于传统的推荐技术具有一定优势,但其缺点是受用户对商品评价的稀疏性制约,现阶段一般利用矩阵填充技术来解决这一问题。主要研究了基于低秩的矩阵填充模型,针对原有模型解对所有奇异值用同一值收缩的问题,提出了一种加权核范数最小化模型以提高核范数灵活度,给出了该模型用收缩算子可得到全局最优解的相关定理及证明,同时对模型的另一种形式在求解过程中的迭代收敛性进行了证明。用凸优化主流算法在两种真实数据集上进行的实验表明,改进后的模型一定程度上提高了计算速度与准确性。
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