摘要
随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差。为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子集中的特定偏好。考虑到参与到不同社交维度的用户对不同的项目感兴趣,首先根据用户的社交关系将用户划分到不同的子集中;其次通过截断奇异值分解技术建模用户对项目的评分,其中全局潜在因子捕获用户共享的层面,而不同用户子集的特定潜在因子捕获用户关心的特定层面;最后,结合全局与局部潜在因子预测用户对未评分项目的评分。实验结果表明该方法可行且有效。
With the development of social networks,more and more studies utilize social information to improve the performance of traditional recommendation algorithms.However,most of the existing recommendation algorithms ignore the diversity of user interests and do not consider the aspects that users care about in different social dimensions,resulting in poor recommendation quality.In order to solve this problem,a recommendation method that considers both global latent factors and the specific latent factors of different subsets is proposed.The recommendation process considers both the user s shared preferences and the user s specific preferences in different subsets.This method first divides users into different subsets according to their social relationships,based on the intuition that users participate in different social dimensions,and are interested in different items.Secondly,the truncated singular value decomposition technique is used to model the user s rating of items,among which the global factors capture levels shared by users,while specific latent factors of different user subsets capture specific levels of user concern.Finally,global and local latent factors are combined to predict user ratings for unscored items.Experiments prove that the method is feasible and effective.
作者
魏云鹤
马慧芳
姜彦斌
宿云
WEI Yun-he;MA Hui-fang;JIANG Yan-bin;SU Yun(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《计算机工程与科学》
CSCD
北大核心
2021年第9期1653-1659,共7页
Computer Engineering & Science
基金
国家自然科学基金(61762078,61363058,61802404)
西北师范大学青年教师能力提升计划(NWNU-LKQN2019-2)
广西可信软件重点实验室研究课题(kx201910)。
关键词
社交信息
共享偏好
特定偏好
截断奇异值分解
social information
shared preference
specific preference
truncated singular value decomposition