摘要
协同过滤是最流行的推荐算法之一,已经成功地应用在很多推荐系统中,而隐语义模型就是协同过滤的典型代表.隐语义模型的核心思想是通过隐类联系用户兴趣和物品,通过矩阵分解技术建立用户和隐类之间的关系,隐类和物品之间的关系,最终得到用户对物品的偏好关系,从而个性化地对用户进行物品的推荐.但是,数据稀疏性和冷启动是协同过滤面临的最大挑战,幸运的是,伴随着社交网络的异军突起,很多学者已经将社交特征数据信息(比如标签、社交等)融入隐语义模型之中来解决协同过滤面临的问题.本文综述了近些年来基于隐语义模型的推荐算法研究成果,总结了常见的基于隐语义模型的推荐算法拓扑结构,并给出了未来的研究方向.
Collaborative filtering is one of the most popular recommendation algorithms, which has been successfully used in many recommendation systems. Among different approaches,latent factor model is a representative one. The core idea of latent factor model is to connect user＇s interests and item by means of the latent classes,then the matrix factorization technology is used to discovery the relationships between the users and the latent classes, the relationships between the latent classes and the items. Finally, we can infer the degree of preference of a user to each item,and provide personalized item recommendations that will suit for this user＇s tastes. However, this approach still suffers from several inherent issues such as data sparsity and cold start. Luckily, along with the rapid development of social network, many scholars have fused the social media data I such as label, social, etc） into the latent factor model to solve the problems that collaborative filtering encounters. In this paper, we review the research of recommendation algorithms based on latent factor model in recent years,summarize the common topologies of the latent factor model,and point out some future research directions.
出处
《小型微型计算机系统》
CSCD
北大核心
2016年第5期881-889,共9页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61272438
61202376
61472253)资助
上海市科委项目(14511107702)资助
上海市教委科研创新项目(13ZZ112
13YZ075)资助
关键词
协同过滤
隐语义模型
矩阵分解
社交网络
数据稀疏性
冷启动
collaborating filtering
latent factor model
matrix factorization
social network
data sparsity
cold start
