基于协同知识图谱特征学习的论文推荐方法

doi:10.19678/j.issn.1000-3428.0056603

摘要/Abstract

摘要： 为解决基于协同过滤的论文推荐方法面对海量数据时存在的数据稀疏性问题，提出一种基于知识图谱表示学习的论文推荐方法。结合开放知识库和用户-论文交互记录构建协同知识图谱，使用基于翻译的知识图谱表示学习算法将用户与论文映射为低维稠密向量表示，通过引入文本信息与结构信息的注意力机制对用户阅读偏好进行建模，并采用聚合函数融合用户邻域特征表示，同时循环使用多层感知机计算用户与论文的相关性得分，从而得到最终论文推荐列表。在CiteULike-a数据集上的实验结果表明，与基于协同过滤、内容过滤和知识图谱的论文推荐方法相比，该方法能有效挖掘论文之间潜在的语义关联关系，提高论文推荐质量。

关键词: 推荐系统, 知识图谱, 论文推荐, 注意力机制, 图邻域

Abstract: Paper recommendation methods based on collaborative filtering suffer from data sparsity when processing massive data.To address the problem,this paper proposes a paper recommendation method based on representation learning of Knowledge Graph(KG).The method constructs a collaborative KG on the basis of open knowledge databases and records of user-paper interactions.Next,an translation-based representation learning algorithm for KG is used to map the user and paper nodes into the representation of low-dimensional dense vectors.Then an attention mechanism for text and structural information is introduced to model the reading preferences of users,and the aggregate function is used to fuse the feature representation of neighborhood nodes of users.Finally,the final recommendation list is obtained based on the correlation scores of users and papers calculated by iteratively using the Multi-Layer Perceptron(MLP).Experimental results on CiteULike-a show that the proposed method outperforms the traditional recommendation methods based on Collaborative Filtering(CF),Content-Based Filtering(CBF) and KG,effectively mining the potential semantic correlations between papers and improving the quality of paper recommendation.

Key words: recommendation system, Knowledge Graph(KG), paper recommendation, attention mechanism, graph neighborhood

中图分类号:

TP319.1

唐浩, 刘柏嵩, 刘晓玲, 黄伟明. 基于协同知识图谱特征学习的论文推荐方法[J]. 计算机工程, 2020, 46(9): 306-312.

TANG Hao, LIU Baisong, LIU Xiaoling, HUANG Weiming. Paper Recommendation Method Based on Feature Learning of Collaborative Knowledge Graph[J]. Computer Engineering, 2020, 46(9): 306-312.

https://www.ecice06.com/CN/Y2020/V46/I9/306

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