摘要
针对基于用户评论的商品推荐算法未充分利用评论之间关联信息的问题,提出一种融合知识图谱与用户评论的商品推荐算法。结合知识图谱对用户评论进行商品特征和情感词提取,构建商品特征集合和商品向量并计算商品相似度矩阵,根据情感词确定商品特征得分,通过随机游走商品节点获取商品特征权重。在此基础上,根据商品特征得分和商品特征权重计算商品推荐价值并进行Top-k推荐。实验结果表明,与基于知识图谱的推荐算法、协同过滤算法、基于内容的推荐算法和混合推荐算法相比,该算法的准确率、召回率和F值最高分别提升15.81%、7.27%和8.55%。
Focused on the issue that the correlation information between user comments is not fully made use of by the existing comment-based commodity recommendation algorithms,this paper proposes a commodity recommendation algorithm fusing with knowledge graph and user comment.The algorithm uses knowledge graph to extract commodity features and emotional words,the commodity feature set and commodity vector are constructed,and the commodity similarity matrix is calculated.Then the commodity feature score is determined according to emotional words,and the weight of a commodity feature is determined by random walking commodity nodes.On the basis of the score and weight of commodity features,the recommended value of the commodity is calculated and thus Top-k recommendation is made.In the comparison experiments with the knowledge graph-based recommendation algorithm,collaborative filtering recommendation algorithm,content-based recommendation algorithm and hybrid recommendation algorithm,results show that the proposed algorithm increases the precision by up to 15.81%,recall rate by up to 7.27%,and F-score by up to 8.55%.
作者
汤伟韬
余敦辉
魏世伟
TANG Weitao;YU Dunhui;WEI Shiwei(School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China;Hubei Provincial Education Informationization Engineering and Technology Center,Wuhan 430062,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第8期93-100,共8页
Computer Engineering
基金
国家自然科学基金(61977021)
湖北省技术创新专项重大项目(2018ACA13)。
关键词
推荐算法
知识图谱
用户评论
商品特征
随机游走模型
recommendation algorithm
knowledge graph
user comment
commodity feature
random walk model
