融合知识图谱和评论文本的个性化推荐模型

A personalized recommendation model integrating knowledge graph and comment text

推荐系统的目的是解决“信息过载”的问题,然而目前的研究方法大多利用用户和商品信息对用户兴趣进行建模,没有同时利用知识图谱构建用户模型来增强推荐系统的性能,因此提出了融合知识图谱和评论文本的个性化推荐模型。首先,通过不同类型的知识图谱分别关联用户项目和用户评论文本,扩展用户的兴趣和提取评论文本中的实体;其次,通过构造用户兴趣网络得到带有用户兴趣偏好的兴趣特征;然后,通过构造画像模块和情感模块的画像网络提取到带有用户情感倾向的画像特征;利用决策层进行点击率预测。最后在Amazon数据集上进行了实验比较,对所提模型的性能进行了评估,并与目前的融合知识图谱和评论文本的推荐模型进行比较,验证了所提模型的有效性。

The purpose of recommender systems is to solve the problem of“information overload”.However,most of the current research methods use user and commodity information to model user interest,and do not consider the use of knowledge map to build user interest and image at the same time to enhance the performance of recommender systems.Therefore,this paper proposes a personalized recommender model integrating knowledge map and comment text.Firstly,different types of know-ledge maps are used to associate user items and user comment texts to expand user interest and extract entities from comment texts.Secondly,interest features with user interest preference are obtained by constructing user interest network.Then,by constructing the portrait network of the portrait module and the emotion module,the portrait features with the user’s emotion tendency are extracted.Finally,the decision-making layer is used to predict the click through rate.The experimental comparison and analysis were carried out on Amazon datasets.Firstly,the recommendation performance of the proposed model is evaluated,and then is compared with the current recommendation model integrating knowledge map and comment text,which verifies the effectiveness of the proposed model.