摘要
人们的生活已经离不开推荐系统了,而推荐算法的优劣则是推动推荐系统发展的重要因素。使用比较广泛的推荐技术有基于内容推荐、协同过滤以及混合推荐。但是以上推荐算法均存在精确率低,覆盖率窄等问题。论文融合了用户的情感因素以及物品的热门程度提出了一种基于潜在因子模型(LFM)的优化算法:基于动量的学习算法,最后通过实验证明改进后的算法比传统的算法在推荐精确度(Accuracy)以及覆盖率(Coverage)上都有明显的提升。
Recommendation system is indispensable to people’s life,and the pros and cons of recommendation algorithm is an important factor to promote the development of recommendation system. Among the more widely used recommendation techniques are association rules,collaborative filtering,and hybrid recommendations. However,the above recommendation algorithms all have problems such as low accuracy rate and narrow coverage. In this paper,an optimization algorithm based on latent factor model(LFM)is proposed by integrating the emotional factors of users and the popularity of items,which is momentum-based learning algorithm. Finally,experiments show that the improved algorithm has significant improvements in the Accuracy and Coverage compared with the traditional algorithm.
作者
孔欢
黄树成
KONG Huan;HUANG Shucheng(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212100)
出处
《计算机与数字工程》
2022年第10期2197-2201,共5页
Computer & Digital Engineering
基金
国家自然科学基金项目“基于鲁棒表现建模的目标跟踪方法研究”(编号:61772244)资助。
关键词
协同过滤
关联规则
混合推荐
潜在因子模型(LFM)
动量
collaborative filtering
association rules
mixed recommendation
latent factor model(LFM)
momentum