[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]
点击复制

基于知识图谱和用户长短期偏好的个性化景点推荐

参考文献/References:
[1] CATHERINE R, COHEN W. Personalized recommendations using knowledge graphs: a probabilistic logic programming approach[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston, Massachusetts, USA, 2016: 325-332.
[2] DI NOIA T, OSTUNI V C, TOMEO P, et al. SPrank: semantic path-based ranking for top-N recommendations using linked open data[J]. ACM transactions on intelligent systems and technology, 2016, 8(1): 9.
[3] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600
LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of computer research and development, 2016, 53(3): 582-600
[4] PALUMBO E, RIZZO G, TRONCY R, et al. Knowledge graph embeddings with node2vec for item recommendation[C]//Proceedings of European Semantic Web Conference. Crete, Greece, 2018: 117-120.
[5] YU Xiao, REN Xiang, SUN Yizhou, et al. Personalized entity recommendation: a heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York, USA, 2014: 283-292.
[6] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[J]. computer science, 2015.
[7] TAN Y K, XU Xinxing, LIU Yong. Improved recurrent neural networks for session-based recommendations[EB/OL]. [2018-10-15] https://arxiv.org/abs/1606.08117, 2016.
[8] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems. Como, Italy, 2017: 130-137.
[9] GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016: 855-864.
[10] MIKOLOV T, SUTSKEVER I, CHEN Kai, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA, 2013: 3111-3119.
[11] CHEN Haochen, PEROZZI B, AL-RFOU R, et al. A tutorial on network embeddings[EB/OL]. [2018-11-15] https://arxiv.org/abs/arXiv:1808.02590, 2018.
[12] TANG Jian, QU Meng, WANG Mingzhe, et al. LINE: large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. Florence, Italy, 2015: 1067-1077.
[13] YANG Cheng, LIU Zhiyuan, ZHAO Deli, et al. Network representation learning with rich text information[C]//Proceedings of the 24th International Conference on Artificial Intelligence. Buenos Aires, Argentina, 2015: 2111-2117.
[14] CAO Shaosheng, LU Wei, XU Qiongkai. GraRep: learning graph representations with global structural information[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia, 2015: 891-900.
[15] RIBEIRO L F R, SAVERESE P H P, FIGUEIREDO D R. 2017. struc2vec: learning node representations from structural identity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax, NS, Canada, 2017: 385-394.
[16] WANG Hongwei, WANG Jia, WANG Jialin, et al. GraphGAN: graph representation learning with generative adversarial nets[C]//Proceedings of AAAI Conference on Artificial Intelligence. New Orleans, Lousiana, USA, 2018: 2508-2515.
[17] WANG Daixin, CUI Peng, ZHU Wenwu. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016: 1225-1234.
[18] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014: 701-710.
[19] ORAMAS S, OSTUNI V C, DI NOIA T, et al. Sound and music recommendation with knowledge graphs[J]. ACM transactions on intelligent systems and technology, 2017, 8(2): 21.
[20] PALUMBO E, RIZZO G, TRONCY R. entity2rec: learning user-item relatedness from knowledge graphs for top-N item recommendation[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems. Como, Italy, 2017: 32-36.
[21] ZHANG Fuzheng, YUAN N J, LIAN Defu, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016: 353-362.
[22] LIN Yankai, LIU Zhiyuan, SUN Maosong, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin Texas, USA, 2015.
[23] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of machine learning research, 2010, 11: 3371-3408.
[24] MASCI J, MEIER U, CIRE?AN D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[C]//Proceedings of the 21st International Conference on Artificial Neural Networks. Espoo, Finland, 2011: 52-59.
[25] LI Jing, REN Pengjie, CHEN Zhumin, et al. Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore, Singapore, 2017: 1419-1428.
[26] LIU Qiao, ZENG Yifu, MOKHOSI R, et al. STAMP: short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, United Kingdom, 2018: 1831-1839.
[27] WU Shu, TANG Yuyuan, ZHU Yanqiao, et al. Session-based recommendation with graph neural networks[J]. Proceedings of the thirty-third AAAI conference on artificial intelligence, 2018, 33(1): 346-353.
[28] 徐增林, 盛泳潘, 贺丽荣, 等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4): 589-606
XU Zenglin, SHENG Yongpan, HE Lirong, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4): 589-606
[29] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. Hong Kong, China, 2001: 285-295.
[30] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Montreal, Quebec, Canada, 2009: 452-461.
相似文献/References:
[1]崔婉秋,杜军平,周南,等.基于用户意图理解的社交网络跨媒体搜索与挖掘[J].智能系统学报,2017,12(6):761.[doi:10.11992/tis.201706075]
 CUI Wanqiu,DU Junping,ZHOU Nan,et al.Social network cross-media searching and mining based on user intention[J].CAAI Transactions on Intelligent Systems,2017,12():761.[doi:10.11992/tis.201706075]
[2]常亮,张伟涛,古天龙,等.知识图谱的推荐系统综述[J].智能系统学报,2019,14(2):207.[doi:10.11992/tis.201805001]
 CHANG Liang,ZHANG Weitao,GU Tianlong,et al.Review of recommendation systems based on knowledge graph[J].CAAI Transactions on Intelligent Systems,2019,14():207.[doi:10.11992/tis.201805001]
[3]王坤,谢振平,陈梅婕.基于图约简的知识联想关系网络建模[J].智能系统学报,2019,14(4):679.[doi:10.11992/tis.201808009]
 WANG Kun,XIE Zhenping,CHEN Meijie.Modeling knowledge network on associative relations based on graph reduction[J].CAAI Transactions on Intelligent Systems,2019,14():679.[doi:10.11992/tis.201808009]
[4]秦娅,申国伟,余红星.基于Hadoop的大规模网络安全实体识别方法[J].智能系统学报,2019,14(5):1017.[doi:10.11992/tis.201809024]
 QIN Ya,SHEN Guowei,YU Hongxing.Large-scale network security entity recognition method based on Hadoop[J].CAAI Transactions on Intelligent Systems,2019,14():1017.[doi:10.11992/tis.201809024]
[5]饶官军,古天龙,常亮,等.基于相似性负采样的知识图谱嵌入[J].智能系统学报,2020,15(2):218.[doi:10.11992/tis.201811022]
 RAO Guanjun,GU Tianlong,CHANG Liang,et al.Knowledge graph embedding based on similarity negative sampling[J].CAAI Transactions on Intelligent Systems,2020,15():218.[doi:10.11992/tis.201811022]
[6]陈新元,谢晟祎,陈庆强,等.结合卷积特征提取和路径语义的知识推理[J].智能系统学报,2021,16(4):729.[doi:10.11992/tis.202008007]
 CHEN Xinyuan,XIE Shengyi,CHEN Qingqiang,et al.Knowledge-based inference on convolutional feature extraction and path semantics[J].CAAI Transactions on Intelligent Systems,2021,16():729.[doi:10.11992/tis.202008007]
[7]罗玲,李硕凯,何清,等.基于知识图谱、TF-IDF和BERT模型的冬奥知识问答系统[J].智能系统学报,2021,16(4):819.[doi:10.11992/tis.202105047]
 LUO Ling,LI Shuokai,HE Qing,et al.Winter Olympic Q & A system based on knowledge map, TF-IDF and BERT model[J].CAAI Transactions on Intelligent Systems,2021,16():819.[doi:10.11992/tis.202105047]
[8]黄庆明,王树徽,许倩倩,等.以图像视频为中心的跨媒体分析与推理[J].智能系统学报,2021,16(5):835.[doi:10.11992/tis.202105042]
 HUANG Qingming,WANG Shuhui,XU Qianqian,et al.Image video centered cross-media analysis and reasoning[J].CAAI Transactions on Intelligent Systems,2021,16():835.[doi:10.11992/tis.202105042]
[9]肖京,王磊,杨余久,等.感知认知技术在金融风险预警中的应用研究[J].智能系统学报,2021,16(5):941.[doi:10.11992/tis.202107027]
 XIAO Jing,WANG Lei,YANG Yujiu,et al.A systematic review of perceptual cognitive technology and its application in the field of financial risk early warning[J].CAAI Transactions on Intelligent Systems,2021,16():941.[doi:10.11992/tis.202107027]
[10]马甜甜,杨长春,严鑫杰,等.融合知识图谱和轻量级图卷积网络推荐系统的研究[J].智能系统学报,2022,17(4):721.[doi:10.11992/tis.202107016]
 MA Tiantian,YANG Changchun,YAN Xinjie,et al.Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system[J].CAAI Transactions on Intelligent Systems,2022,17():721.[doi:10.11992/tis.202107016]
[11]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.[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():430.[doi:10.11992/tis.201810032]

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
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com