ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (3): 562-575.doi: 10.7544/issn1000-1239.2020.20190189

• 人工智能 • 上一篇    下一篇



  1. 1(新疆大学信息科学与工程学院 乌鲁木齐 830046);2(新疆大学软件学院 乌鲁木齐 830008) (
  • 出版日期: 2020-03-01
  • 基金资助: 

A Feature Extraction Based Recommender Algorithm Fusing Semantic Analysis

Chen Jiaying1, Yu Jiong1, Yang Xingyao2   

  1. 1(School of Information Science and Engineering, Xinjiang University, Urumqi 830046);2(School of Software, Xinjiang University, Urumqi 830008)
  • Online: 2020-03-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61862060, 61462079, 61562086, 61562078).

摘要: 推荐系统是大数据环境下解决用户个性化推荐的关键技术.针对现有推荐算法所面临的难以分析提取用户、项目本质特征的问题,将知识图谱作为异质信息融入协同过滤推荐算法进行项目语义特征分析,提出一种融合语义分析特征提取的推荐算法.首先根据推荐平台中项目的非结构化评论文本信息,结合知识图谱利用实体识别与连接技术在知识库中提取项目特征相关实体与关系,构建子知识库;然后通过知识图谱表示学习方法对子知识库进行表示学习,并将其用于项目和用户的低维向量表示;设计知识感知的协同学习框架,定义损失函数优化用户、项目的细粒度特征向量;最后根据用户、项目表征结果对目标用户进行Top-N推荐.在2个数据集上进行验证实验,结果表明:改进的算法在推荐准确率、召回率方面优于对比算法,能够为用户推荐更符合其偏好的项目.

关键词: 推荐系统, 语义分析, 细粒度特征, 知识感知, 协同学习

Abstract: Recommender system is an effective way to deal with the problem of personalized recommendations. Most existing recommendation methods have insufficient power to analysize inherent characteristics of users and items. To alleviate the problem, a feature extraction based recommender algorithm that fuses semantic analysis is proposed in this paper, which involves knowledge graph as heterogeneous information to enhance semantic analysis of collaborative filtering. First of all, the named entity recognition (NER) and entity linking (EL) are used to extract entities and relations about a certain item from its unstructured text information, and we construct a subgraph based on these identified entities and relations. Then we embed the subgraph to a low latent vector space by the technology of knowledge graph embedding for an easier expression. After that, the embedding results are used to represent users and items, and we design a knowledge aware collaborative learning framework to learn the fine-grained features of users and items. Finally, the embedding results are used to make Top-N recommendations for a target user. Experimental results based on two datasets show that our new framework is able to improve the recommender accuracy compared with some state-of-the-art models. It means that our new method is able to recommender items which are better matches in users’ preferences.

Key words: recommender system, semantic analysis, fine-grained feature, knowledge-aware, collaborative learning