计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 468-472.
刘晴晴, 罗永龙, 汪逸飞, 郑孝遥, 陈文
LIU Qing-qing, LUO Yong-long, WANG Yi-fei, ZHENG Xiao-yao, CHEN Wen
摘要： 随着互联网技术的发展,信息过载问题日益严重,推荐系统是缓解该问题的有效手段。针对协同过滤中因数据稀疏和冷启动导致的推荐效率低下问题,提出基于SVD填充的混合推荐算法。首先,采用奇异值分解技术分解项目评分矩阵,通过随机梯度下降法填充稀疏矩阵;然后,在矩阵中加入时间权重,优化用户相似度,同时在项目矩阵中加入Jaccard系数优化项目相似度;接着,综合基于项目和基于用户的协同过滤计算预测评分,从而选择最优项目;最后,在MovieLens和Jester数据集中将所提算法与传统算法进行实验对比,证明了所提算法的有效性。
中图分类号:
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