摘要
在长尾推荐场景中,目标用户更信任与自己兴趣相似的好友的推荐结果,故为目标用户推荐其好友的个性化偏好物品有利于提高长尾推荐性能.相应地,如何有效融合社交网络信息与评分矩阵信息,提升推荐性能自然成为长尾推荐中的重要问题.为此,文中从信息融合视角出发,通过社交网络和评分矩阵共享用户的潜在特征向量,并将好友推荐信息作为长尾推荐的重要影响因素,建立融合社交网络信息的长尾推荐方法.将用户活跃度、项目非流行度、用户项目偏好水平及好友推荐行为作为输入,采用变分推断方法,得出模型中相关未知参数,实现预测功能.实验表明,文中方法能在有效实现长尾物品推荐的同时,保证较高的推荐精度.
In the long-tail recommendation scenario,target users are more likely to trust the recommendation results of the friends with similar interests.Therefore,recommending personalized preferences of friends to target users is conducive to improving the performance of long-tail recommendation methods.Accordingly,how to effectively fuse social network information with rating matrix information is an important issue in long-tail recommendation.In this paper,a long-tail recommendation method based on social network information is designed.From the perspective of information fusion,social network and rating matrix information are utilized to share potential feature vectors of users.The information of friend recommendation is taken as an important factor in the proposed recommendation model.User activity level,item unpopularity level,user-item preference level and friend recommendation behavior are taken as inputs,and variational inference is employed to get relevant unknown parameters to realize accurate prediction.Experiments show that the proposed method can recommend long-tail items effectively with high recommendation accuracy.
作者
冯晨娇
宋鹏
张凯涵
梁吉业
FENG Chenjiao;SONG Peng;ZHANG Kaihan;LIANG Jiye(College of Applied Mathematics,Shanxi University of Finance and Economics,Taiyuan 030006;School of Economics and Management,Shanxi University,Taiyuan 030006;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006)
出处
《模式识别与人工智能》
CSCD
北大核心
2022年第1期26-36,共11页
Pattern Recognition and Artificial Intelligence
基金
国家重点研发计划项目(No.2020AAA0106100)
国家自然科学基金项目(No.72171137,61906111)
山西省重点实验室开放基金项目(No.CICIP2020005)资助。
关键词
推荐系统
长尾推荐
社会化推荐
概率图模型
Recommender System
Long-Tail Recommendation
Social Recommendation
Probabilistic Graphical Model
