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[1]贾中浩,古天龙,宾辰忠,等.旅游知识图谱特征学习的景点推荐[J].智能系统学报,2019,14(03):430-437.[doi:10.11992/tis.201810032]
 JIA Zhonghao,GU Tianlong,BIN Chenzhong,et al.Tourism knowledge-graph feature learning for attraction recommendations[J].CAAI Transactions on Intelligent Systems,2019,14(03):430-437.[doi:10.11992/tis.201810032]
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旅游知识图谱特征学习的景点推荐(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
430-437
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Tourism knowledge-graph feature learning for attraction recommendations
作者:
贾中浩 古天龙 宾辰忠 常亮 张伟涛 朱桂明
桂林电子科技大学 广西可信软件重点实验室, 广西 桂林 541004
Author(s):
JIA Zhonghao GU Tianlong BIN Chenzhong CHANG Liang ZHANG Weitao ZHU Guiming
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
知识图谱属性子图特征学习神经网络景点推荐网络嵌入推荐算法深度学习
Keywords:
knowledge graphattribution subgraphfeature learningneural networkattractions recommendationnetwork embeddingrecommendation algorithmdeep learning
分类号:
TP301
DOI:
10.11992/tis.201810032
摘要:
基于知识图谱的推荐算法在多个领域取得了较好的效果,但仍然存在一些问题,如不能有效提取知识图谱中实体关系标签中的特征,推荐准确率会降低。因而提出将网络嵌入方法(network embedding)用于旅游知识图谱的特征提取,使得特征的提取更加充分。通过对旅游知识图谱中不同标签的属性子图独立建模,利用深度学习模型挖掘游客及景点等图节点语义特征,进而获得融合各个标签语义的游客和景点特征向量,最终通过计算游客和景点相关性生成景点推荐列表。通过在真实旅游知识图谱上的实验,验证了利用网络嵌入方法对知识图谱中数据建模后,可以有效提取节点的深层特征。
Abstract:
The recommendation algorithm based on knowledge graphs has achieved good results in several fields; however, it contains some problems as well. For example, this algorithm cannot effectively extract features from entity relationship tags in the knowledge graph, which reduces its recommendation accuracy. We propose a network embedding method that more fully extracts features from the tourism knowledge graph than the aforementioned method. By independently modeling different label-attribute subgraphs in the tourism knowledge graph and by using a deep learning model to mine node semantic features, such as tourists and scenic spots, we can obtain a feature vector of tourists and attractions that fuses the semantics of various tags. Finally, this method generates a recommended list of scenic spots by calculating the correlation between tourists and scenic spots. Experimental results on a real tourism knowledge graph verify that this network embedding method effectively extracts the deep features of knowledge graph nodes to model the data in the knowledge graph.

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备注/Memo

备注/Memo:
收稿日期:2018-10-26。
基金项目:国家自然科学基金项目(U1711263,U1501252,61572146);广西省自然科学基金项目(2016GXNSFDA380006,AC16380122);广西创新驱动重大专项项目(AA17202024);广西高校中青年教师基础能力提升项目(2018KYD203);广西研究生教育创新计划项目(2019YCXS042,2019YCXS041).
作者简介:贾中浩,男,1995年生,硕士研究生,主要研究方向为机器学习、推荐系统;古天龙,男,1964年生,教授,博士生导师,博士,主要研究方向为形式化方法、知识工程与符号推理、协议工程与移动计算、可信泛在网络、嵌入式系统。主持国家863计划项目、国家自然科学基金项目、国防预研重点项目、国防预研基金项目等30余项。发表学术论文130余篇,被SCI、EI检索60余篇,出版学术著作3部;宾辰忠,男,1979年生,讲师,博士研究生, 主要研究方向为数据挖掘、智能推荐。
通讯作者:宾辰忠.E-mail:binchenzhong@guet.edu.cn
更新日期/Last Update: 1900-01-01