[1]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):207-216.[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(2):207-216.[doi:10.11992/tis.201805001]
点击复制
CHANG Liang,ZHANG Weitao,GU Tianlong,et al.Review of recommendation systems based on knowledge graph[J].CAAI Transactions on Intelligent Systems,2019,14(2):207-216.[doi:10.11992/tis.201805001]
知识图谱的推荐系统综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第2期
页码:
207-216
栏目:
综述
出版日期:
2019-03-05
- Title:
- Review of recommendation systems based on knowledge graph
- Keywords:
- knowledge graph; recommendation system; ontology; linked open data; graph embedding; network representation learning; similarity; prediction score
- 分类号:
- TP301
- 摘要:
- 如何为用户提供个性化推荐并提高推荐的准确度和用户满意度，是当前推荐系统研究面临的主要问题。知识图谱的出现为推荐系统的改进提供了新的途径。本文研究了知识图谱近年来在推荐系统中的应用情况，从基于本体的推荐生成、基于开放链接数据的推荐生成以及基于图嵌入的推荐生成3个方面对研究现状进行了综述。在此基础上，提出了基于知识图谱的推荐系统总体框架，分析了其中涉及的关键技术，并对目前存在的重点和难点问题进行了讨论，指出了下一步需要开展的研究工作。
- Abstract:
- In current research on recommendation systems, the provision of personalized recommendations to users and the improvement of the accuracy and user satisfaction of recommendations are main concerns. The emergence of knowledge graphs provides a new way to improve recommendation systems. The applications of knowledge graphs to recommendation systems in recent years are summarized in this paper, and the current status of the research is investigated in detail from three aspects:ontology-based recommendation generation, recommendation generation based on linked open data, and recommendation generation based on graph embedding. On this basis, this paper proposes the general framework of recommendation systems based on knowledge graph, analyzes the key technologies involved, discusses the existing key issues and difficulties, and indicates the further research work to be carried out.
- 参考文献/References:
-
[1] XIONG Haitao, LIU Zhengbin. A situation information integrated personalized travel package recommendation approach based on TD-LDA model[C]//2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC). Nanjing, China:IEEE, 2015:32-37.
[2] 常亮, 曹玉婷, 孙文平, 等. 旅游推荐系统研究综述[J]. 计算机科学, 2017, 44(10):1-6 CHANG Liang, CAO Yuting, SUN Wenping, et al. Review of tourism recommendation system[J]. Computer science, 2017, 44(10):1-6
[3] 孟祥武, 纪威宇, 张玉杰. 大数据环境下的推荐系统[J]. 北京邮电大学学报, 2015, 38(2):1-15 MENG Xiangwu, JI Weiyu, ZHANG Yujie. A survey of recommendation systems in big data[J]. Journal of Beijing university of posts and telecommunications, 2015, 38(2):1-15
[4] 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 Yu, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of computer research and development, 2016, 53(3):582-600
[5] LUBERG A, TAMMET T, J?RV P. Smart city:A rule-based tourist recommendation system[M]//LAW R, FUCHS M, RICCI F. Information and Communication Technologies in Tourism 2011. Vienna, Austria:Springer, 2011:51-62.
[6] TONG Rong, XUE Lijuan, WANG Haofen, et al. Building and exploring an enterprise knowledge graph for investment analysis[M]//GROTH P, SIMPERL E, GRAY A, et al. The Semantic Web-ISWC 2016. Cham, Germany:Springer International Publishing, 2016:418-436.
[7] 漆桂林, 高桓, 吴天星. 知识图谱研究进展[J]. 情报工程, 2017, 3(1):4-25 QI Guilin, GAO Huan, WU Tianxing. The research advances of knowledge graph[J]. Technology information engineering, 2017, 3(1):4-25
[8] SZEKELY P, KNOBLOCK C A, SLEPICKA J, et al. Building and using a knowledge graph to combat human trafficking[C]//2015 International Semantic Web Conference on. New York, USA:Springer-Verlag, 2015:205-221.
[9] 唐晓波, 魏巍. 基于本体的推荐系统研究综述[J]. 图书馆学研究, 2016(18):7-12, 58 TANG Xiaobo, WEI Wei. A survey of ontology-based recommendation systems[J]. Library science studies, 2016(18):7-12, 58
[10] LI Dongsheng, LV Qin, XIE Xing, et al. Interest-based real-time content recommendation in online social communities[J]. Knowledge-based systems, 2012, 28:1-12.
[11] 印鉴, 王智圣, 李琪, 等. 基于大规模隐式反馈的个性化推荐[J]. 软件学报, 2014, 25(9):1953-1966 YIN Jian, WANG Zhisheng, LI Qi, et al. Personalized recommendation based on large-scale implicit feedback[J]. Journal of software, 2014, 25(9):1953-1966
[12] 王立才, 孟祥武, 张玉洁. 上下文感知推荐系统[J]. 软件学报, 2012, 23(1):1-20 WANG Licai, MENG Xiangwu, ZHANG Yujie. Context-aware recommendation systems[J]. Journal of software, 2012, 23(1):1-20
[13] NIARAKI A S, KIM K. Ontology based personalized route planning system using a multi-criteria decision making approach[J]. Expert systems with applications, 2009, 36(2):2250-2259.
[14] DODWAD P R, LOBO L. A context-aware recommender system using ontology based approach for travel applications[J]. International journal of advanced engineering and nano technology, 2014, 1(10):8-12.
[15] KETHAVARAPU U P K, SARASWATHI S. Concept based dynamic ontology creation for job recommendation system[J]. Procedia computer science, 2016, 85:915-921.
[16] MORENO A, VALLS A, ISERN D, et al. SigTur/E-destination:Ontology-based personalized recommendation of tourism and leisure activities[J]. Engineering applications of artificial intelligence, 2013, 26(1):633-651.
[17] PASSANT A. dbrec-music recommendations using DBpedia[M]//PATEL-SCHNEIDER P F, PAN Yue, HITZLER P, et al. The Semantic Web-ISWC 2010. Berlin Heidelberg, Germany:Springer, 2010:209-224.
[18] DI NOIA T, MIRIZZI R, OSTUNI V C, et al. Linked open data to support content-based recommender systems[C]//Proceedings of the 8th International Conference on Semantic Systems. Graz, Austria:ACM, 2012:1-8.
[19] DI NOIA T, CANTADOR I, OSTUNI V C. Linked open data-enabled recommender systems:ESWC 2014 challenge on book recommendation[M]//PRESUTTI V, STANKOVIC M, CAMBRIA E, et al. Semantic Web Evaluation Challenge. Cham, Germany:Springer International Publishing, 2014:129-143.
[20] LU Chun, LAUBLET P, STANKOVIC M. Travel attractions recommendation with knowledge graphs[C]//European Knowledge Acquisition Workshop. Bologna, Italy:Springer, 2016.
[21] ORAMAS S, OSTUNI V C, DI NOIA T, et al. Sound and music recommendation with knowledge graphs[J]. ACM transactions on intelligent systems & technology, 2017, 8(2):21.
[22] HEITMANN B, HAYES C. Using linked data to build open, collaborative recommender systems[C]//Linked Data Meets Artificial Intelligence. Stanford, California, USA:AAAI, 2010.
[23] OSTUNI V C, DI NOIA T, DI SCIASCIO E, et al. Top-N recommendations from implicit feedback leveraging linked open data[C]//Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong, China:ACM, 2013:85-92.
[24] RISTOSKI P, MENCIA E L, PAULHEIM H. A hybrid multi-strategy recommender system using linked open data[C]//Semantic Web Evaluation Challenge. Cham, Germany:Springer, 2014, 475:150-156.
[25] TING K M, WITTEN I H. Issues in stacked generalization[J]. Artificial intelligence research, 1999,10(1).
[26] PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk:Online learning of social representations[C]//ACM, 2014,701-710.
[27] GRAD-GYENGE L, KISS A, FILZMOSER P. Graph embedding based recommendation techniques on the knowledge graph[C]//Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. Bratislava, Slovakia:ACM, 2017:354-359.
[28] WANG Meng, LIU Mengyue, LIU Jun, et al. Safe medicine recommendation via medical knowledge graph embedding[EB/OL]. arXiv:1710.05980, 2017.
[29] PALUMBO E, RIZZO G, TRONCY R. entity2rec:Learning user-item relatedness from knowledge graphs for top-N item recommendation[C]//Eleventh ACM Conference on Recommender Systems. Como, Italy:ACM, 2017:32-36.
[30] GRAD-GYENGE L, FILZMOSER P. Recommendation Techniques on a Knowledge Graph for Email Remarketing[C]//eKNOW 2016 The Eighth International Conference on Information, Process, and Knowledge Management. Venice, Italy:IARIA, 2016.
[31] LIU Xiaohua, ZHANG Shaodian, WEI Furu, et al. Recognizing named entities in tweets[C]//49th Annual Meeting of the Association for Computational Linguistics; Human Language Technologies. Stroudsburg, PA, USA:ACL, 2011:359-367.
[32] FELLBAUM C. WordNet[M]//The Encyclopedia of Applied Linguistics. Blackwell:Blackwell Publishing Ltd, 2012:231-243.
[33] WANG Zhigang, LI Juanzi, LI Shuanjie, et al. Cross-lingual knowledge validation based taxonomy derivation from heterogeneous online wikis[C]//28th Conference on Artificial Intelligence. Menko Park, USA:AAAI, 2014:180-186.
[34] DESHPANDE O, LAMBA D S, TOURN M, et al. Building, maintaining, and using knowledge bases:A report from the trenches[C]//the 32nd ACM SIGMOD International Conference on Management of Data. New York, USA:ACM, 2013:1209-1220
[35] ZHANG Daqiang, HSU C H, CHEM Min, et al. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems[J]. IEEE transactions on emerging topics in computing, 2014, 2(2):239-250.
[36] HSIEH K L. Employing a recommendation expert system based on mental accounting and artificial neural networks into mining business intelligence for study abroad’s P/S recommendations[J]. Expert systems with applications, 2011, 38(12):14376-14381.
[37] 薛福亮, 马莉. 利用动态产品分类树改进的关联规则推荐方法[J]. 计算机工程与应用, 2016, 52(4):135-141 XUE Fuliang, MA Li. Improved association rule recommendation method based on dynamic product taxonomy[J]. Computer engineering and applications, 2016, 52(4):135-141
[38] LU E H C, FANG S H, TSENG V S. Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints[J]. GeoInformatica, 2016, 20(4):741-763.
[39] LI Ting, LIU Anfeng, HUANG Changqin. A similarity scenario-based recommendation model with small disturbances for unknown items in social networks[J]. IEEE Access, 2017, 4:9251-9272.
[40] GRüN C, NEIDHARDT J, WERTHNER H. Ontology-based matchmaking to provide personalized recommendations for tourists[M]//SCHEGG R, STANGL B. Information and Communication Technologies in Tourism 2017. Cham:Springer International Publishing, 2017.
[41] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2):163-175 ZHU Yuxi, Lü Linyuan. Evaluation metrics for recommender systems[J]. Journal of university of electronic science and technology, 2012, 41(2):163-175
[42] VARGAS S, CASTELLS P. Rank and relevance in novelty and diversity metrics for recommender systems[C]//5th ACM Conference on Recommender Systems. Chicago, USA:ACM, 2011:109-116.
[43] CATHERINE R, COHEN W. Personalized recommendations using knowledge graphs:A probabilistic logic programming approach[C]//10th ACM Conference on Recommender Systems. Boston, USA:ACM, 2016:325-332.
[44] LI Haoyang, WU Yuanxu, XIA Wei. Review on state-of-the-art technologies and algorithms on recommendation system[C]//2016 International Conference on Mechatronics Engineering and Information Technology. 2016.
[45] MAYER-SCHNBERGER V, CUKIER K. Big data:A revolution that will transform how we live, work, and think[M]. Eamon Dolan/Houghton Mifflin Harcourt, 2014.
[46] KETHAVARAPU U P K, SARASWATHI S. Concept based dynamic ontology creation for job recommendation system[J]. Procedia computer science, 2016, 85:915-921.
[47] OSTUNI V C, DI NOIA T, MIRIZZI R, et al. A linked data recommender system using a neighborhood-based graph kernel[M]//International Conference on Electronic Commerce and Web Technologies. Cham:Springer International Publishing, 2014:89-100.
[48] FENG Xiaodong, SHARMA A, SRIVASTAVA J, et al. Social network regularized sparse linear model for Top-N, recommendation[J]. Engineering applications of artificial intelligence, 2016, 51:5-15.
[49] LIU Juntao, WU Caihua. Deep learning based recommendation:a survey[M]//International Conference on Information Science and Applications 2017. Singapore:Springer, 2017.
[50] WANG Quan, MAO Zhendong, WANG Bin, et al. Knowledge graph embedding:A survey of approaches and applications[J]. IEEE transactions on knowledge and data engineering, 2017, 29(12):2724-2743.
- 相似文献/References:
-
[1]黄立威,李德毅.社交媒体中的信息推荐[J].智能系统学报,2012,7(1):1.
HUANG Liwei,LI Deyi.A review of information recommendation in social media[J].CAAI Transactions on Intelligent Systems,2012,7():1.
[2]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]
[3]郭少成,陈松灿.稀疏化的因子分解机[J].智能系统学报,2017,12(6):816.[doi:10.11992/tis.201706030]
GUO Shaocheng,CHEN Songcan.Sparsified factorization machine[J].CAAI Transactions on Intelligent Systems,2017,12():816.[doi:10.11992/tis.201706030]
[4]马钰,张岩,王宏志,等.面对智能导诊的个性化推荐算法[J].智能系统学报,2018,13(3):352.[doi:10.11992/tis.201711036]
MA Yu,ZHANG Yan,WANG Hongzhi,et al.A personalized recommendation algorithm for intelligent guidance[J].CAAI Transactions on Intelligent Systems,2018,13():352.[doi:10.11992/tis.201711036]
[5]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]
[6]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]
[7]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]
[8]吴国栋,查志康,涂立静,等.图神经网络推荐研究进展[J].智能系统学报,2020,15(1):14.[doi:10.11992/tis.201908034]
WU Guodong,ZHA Zhikang,TU Lijing,et al.Research advances in graph neural network recommendation[J].CAAI Transactions on Intelligent Systems,2020,15():14.[doi:10.11992/tis.201908034]
[9]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]
[10]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.[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():990.[doi:10.11992/tis.201904064]
[11]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]
备注/Memo
收稿日期:2018-05-02。
基金项目:国家自然科学基金项目（61572146，U1501252，U1711263）；广西创新驱动重大专项项目（AA17202024）；广西自然科学基金项目（2016GXNSFDA380006）.
作者简介:常亮，男，1980年生，教授，博士，中国计算机学会高级会员，主要研究方向为数据与知识工程、形式化方法、智能系统。主持并完成多项科研项目，其中国家自然科学基金项目1项、广西自然科学基金项目1项。发表学术论文70余篇，被SCI、EI收录60余篇。;张伟涛，男，1993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、国防预研重点项目、国防预研基金等30余项。发表学术论文130余篇，被SCI、EI收录60余篇，出版学术著作3部。
通讯作者:宾辰忠.E-mail:binchenzhong@guet.edu.cn
更新日期/Last Update:
2019-04-25