计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 189-193.
朱峙成1, 刘佳玮1,2, 阎少宏1,2
ZHU Zhi-cheng1, LIU Jia-wei1,2, YAN Shao-hong1,2
摘要: 传统的智能推荐中运用了协同过滤算法,但是它并不能很好地处理用户的评分信息,推荐的质量受存在的数据稀疏性、极端数据的影响。对此,将推荐问题转换为多标签学习问题,文中提出了一种基于HMM模型和用户画像的完备智能推荐系统。首先设立不同的数据处理机制来提高模型的泛化能力,其次为了解决数据稀疏问题,提出反马尔科夫性改进HMM模型,最终构建用户画像对HMM模型的学习经验得到的结果进行筛选,得到最终的推荐服务。实验结果表明,在智能推荐问题中多标签学习有效地提高了推荐准确性和推荐效率。
中图分类号:
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