[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(3):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(3):430-437.[doi:10.11992/tis.201810032]
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JIA Zhonghao,GU Tianlong,BIN Chenzhong,et al.Tourism knowledge-graph feature learning for attraction recommendations[J].CAAI Transactions on Intelligent Systems,2019,14(3):430-437.[doi:10.11992/tis.201810032]
旅游知识图谱特征学习的景点推荐
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第3期
页码:
430-437
栏目:
学术论文—知识工程
出版日期:
2019-05-05
- Title:
- Tourism knowledge-graph feature learning for attraction recommendations
- Keywords:
- knowledge graph; attribution subgraph; feature learning; neural network; attractions recommendation; network embedding; recommendation algorithm; deep learning
- 分类号:
- TP301
- 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.
- 参考文献/References:
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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