[1]程华松,熊才权,柯远志,等. 基于知识图谱的新闻推荐神经网络模型[J].湖北工业大学学报,2023,(4):82-87.
　CHENG Huasong,XIONG Caiquan,KE Yuanzhi,et al. News Recommendation Neural Network Model Based on Knowledge Graph[J].,2023,(4):82-87.
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 基于知识图谱的新闻推荐神经网络模型()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2023年第4期
页码:
82-87
栏目:
湖北工业大学学报
出版日期:
2023-08-30

文章信息/Info

Title:
 News Recommendation Neural Network Model Based on Knowledge Graph
文章编号:
1003-4684(2023)04-0082-06
作者:
 程华松; 熊才权;  柯远志;  吴歆韵
 湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
 CHENG Huasong;  XIONG Caiquan;  KE Yuanzhi;  WU Xinyun
 School of Computer Science, Hubei Univ. of Tech., Wuhan 430068, China
关键词:
 ; 新闻推荐;  神经网络;  知识图谱;  注意力机制
Keywords:
 ; news recommendation;  neural networks;  knowledge graph;  attention mechanism
分类号:
TP18
文献标志码:
A
摘要:
 新闻推荐的目标是根据用户的历史阅读习惯，为用户推送其感兴趣的新闻内容。现有的方法存在特征学习不足的问题，没有考虑到用户与浏览新闻之间的关系，以及不同候选新闻对用户的重要性。针对以上问题提出一种基于知识图谱的新闻推荐神经网络模型。首先使用GloVe模型（Global Vectors for Word Representation）和基于翻译的模型（TransX）分别从新闻语义和知识层面得到文本语义特征、实体特征和实体在知识图谱中的上下文特征。然后，使用LSTM-CNN网络挖掘新闻和用户深层次信息，得到用户的新闻偏好向量，同时引入注意力机制减少新闻无关题材的影响；最后通过点击求和计算用户对候选新闻的偏好值并生成最终的推荐结果。在真实新闻数据集MIND上的实验结果表明，本文所提出的模型相对现有的DKN、DeepFM、DeepWide模型，在AUC、MRR、NDCG@k指标上表现的更优异。
Abstract:
 The goal of news recommendation is to push the news content of interest to users based on their historical reading habits. Existing methods suffer from insufficient feature learning and do not consider the relationship between users and browsing news, as well as the importance of different candidate news to users. Aiming at the above problems, this paper proposes a news recommendation neural network model based on knowledge graph. First, the Glove model (global log bilinear regression model) and the translation-based model (TransX) are used to obtain text semantic features, entity features, and context features of entities in the knowledge graph from news semantics and knowledge levels, respectively. Then, the LSTM-CNN network is used to mine the deep-level information of news and users to obtain the user’s news preference vector, and at the same time, an attention mechanism is introduced to reduce the influence of irrelevant news topics; Finally, the user’s preference value for candidate news is calculated by clicking the summation and the final news is generated recommended results. The experimental results on the real news dataset MIND show that the model proposed in this paper performs better in terms of AUC, MRR, and nDCG@k than the existing DKN, DeepFM, and DeepWide models.

参考文献/References:

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备注/Memo

备注/Memo:
 ［收稿日期］ 2022-04-14
［基金项目］ 湖北省支持企业技术创新发展项目（2021BLB171）；国家自然科学基金（61902116）
［第一作者］ 程华松（1997-），男，湖北黄冈人，湖北工业大学硕士研究生，研究方向为人工智能。
［通信作者］ 熊才权（1966-），男，湖北鄂州人，工学博士，湖北工业大学教授，研究方向为人工智能，辩论模型，智能决策。
更新日期/Last Update: 2023-08-26