[1]贾中浩,宾辰忠,古天龙,等.基于知识图谱和用户长短期偏好的个性化景点推荐[J].智能系统学报,2020,15(5):990-997.[doi:10.11992/tis.201904064]
 JIA Zhonghao,BIN Chenzhong,GU Tianlong,et al.Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences[J].CAAI Transactions on Intelligent Systems,2020,15(5):990-997.[doi:10.11992/tis.201904064]
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基于知识图谱和用户长短期偏好的个性化景点推荐(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
990-997
栏目:
学术论文—知识工程
出版日期:
2020-09-05

文章信息/Info

Title:
Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences
作者:
贾中浩 宾辰忠 古天龙 常亮 朱桂明 陈炜
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 BIN Chenzhong GU Tianlong Chang Liang Zhu Guiming Chen Wei
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
知识图谱推荐算法网络表示学习门控循环单元个性化景点推荐长短期用户偏好特征学习
Keywords:
knowledge graphrecommendation algorithmnetwork representation learninggated recurrent unitpersonalized attractions recommendationusers’ long-term and short-term preferencefeature learning
分类号:
TP301
DOI:
10.11992/tis.201904064
文献标志码:
A
摘要:
基于序列化的推荐算法在多个领域取得了不错的效果,但仍存在一些问题,如没有考虑所有项与项之间的关系,推荐准确度会大大降低。因此提出一种基于知识图谱和用户长短期偏好(KG-ULSP)的个性化景点推荐方法。通过引入知识图谱,使用网络表示学习方法,学习景点的特征向量表示,使得具有相似结构和相似属性的景点在低维特征空间中的距离比较近,以此表示他们的高级语义特征。然后利用门控循环单元GRU对已学习到的景点特征向量进行序列化信息建模,进一步抽取景点的访问序列特征。另外,考虑到用户偏好可能随时间发生变化,KG-ULSP模型同时学习用户的长期偏好和短期偏好,最终预测并返回用户可能感兴趣的推荐列表。通过在真实旅游数据上的实验,验证了所提方法的有效性。
Abstract:
The session-based recommendation algorithm has achieved good results in many fields. However, several problems, such as not considering the relationship between all items, will reduce the recommendation accuracy considerably. Therefore, a personalized attraction recommendation method based on the knowledge graph and users’ long-term and short-term preferences (KG-ULSP) is proposed. The knowledge graph is derived using the network representation learning method and the feature vector representation of the learning attractions. The attractions with similar structure and attribute are close to each other in the low-dimensional space and express high-level semantic features. In addition, the sequence information is modeled by the gated recurrent unit and the access sequence information is further extracted by feature extraction. Moreover, given that the users’ preferences may change with time, the KG-ULSP model learns both long-term and short-term preferences of the user and predicts and returns the list of recommendations that users may be interested in. The validity of the proposed method is verified by experiments on real tourism data.

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备注/Memo

备注/Memo:
收稿日期:2019-04-26。
基金项目:国家自然科学基金项目(U1711263,U1501252,61572146);广西自然科学基金项目(2016GXNSFDA380006,AC16380122,AA17202024);广西高校中青年教师基础能力提升项目(2018KY0203);广西研究生教育创新计划项目(2019YCXS042,2019YCXS041)
作者简介:贾中浩,硕士研究生,主要研究方向为机器学习、推荐系统;宾辰忠,博士研究生,主要研究方向为数据挖掘、智能推荐;古天龙,教授,博士生导师,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、国防预研重点项目、国防预研基金等30余项。出版学术著作3部,发表学术论文130余篇。
通讯作者:宾辰忠.E-mail:binchenzhong@guet.edu.cn
更新日期/Last Update: 2021-01-15