计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 36-43.doi: 10.11896/j.issn.1002-137X.2019.04.006
李红梅1, 刁兴春1, 曹建军2, 冯钦1, 张磊1
LI Hong-mei1, DIAO Xing-chun1, CAO Jian-jun2, FENG Qin1, ZHANG Lei1
摘要: 为进一步提高面向隐式反馈的标签感知推荐性能,针对隐式反馈数据的稀疏性问题以及标签数据的冗余、语义模糊等问题,提出了一种基于用户细粒度偏好和增量加权矩阵分解的个性化推荐方法。为缓解隐式反馈数据稀疏不平衡的影响,提出使用协同近邻用户关系从大规模未观测数据中挖掘目标用户可能感兴趣的潜在项目,即近邻用户感兴趣但目标用户未选择的项目,进而提出了用户对项目的细粒度偏好假设:观测项目>潜在项目>其他未观测项目,改进传统成对偏好假设的粗糙性。为获取更为可靠的近邻用户,利用基于深度学习的方法来抽取用户-标签的低维、抽象的深层语义特征,缓解了原始标签数据的冗余、语义模糊等对用户表征的影响。最后,基于用户的细粒度偏好提出一种增量加权矩阵分解模型,并进行快速优化求解与推荐。实验结果表明:提出的算法在多个排序推荐准确性的评价指标(Pre@5,NDCG@5,MRR)上分别提升了约9%,8%,9%,验证了所提算法的有效性。
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