[1]YOCHUM Phatpicha,常亮,古天龙,等.基于位置和开放链接数据的旅游推荐系统综述[J].智能系统学报,2020,15(1):25-32.[doi:10.11992/tis.201912023]
 YOCHUM Phatpicha,CHANG Liang,GU Tianlong,et al.A review of linked open data in location-based recommendation system in the tourism domain[J].CAAI Transactions on Intelligent Systems,2020,15(1):25-32.[doi:10.11992/tis.201912023]
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基于位置和开放链接数据的旅游推荐系统综述

参考文献/References:
[1] DE PESSEMIER T, DHONDT J, MARTENS L. Hybrid group recommendations for a travel service[J]. Multimedia tools and applications, 2017, 76(2): 2787–2811.
[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
[3] ZIMBA B, CHIBUTA S, CHISANGA D, et al. Point of interest recommendation methods in location based social networks: traveling to a new geographical region[J]. arXiv: 1711.09471, 2017.
[4] WANG Donghui, LIANG Yanchun, XU Dong, et al. A content-based recommender system for computer science publications[J]. Knowledge-based systems, 2018, 157: 1–9.
[5] FU Mingsheng, QU Hong, YI Zhang, et al. A novel deep learning-based collaborative filtering model for recommendation system[J]. IEEE transactions on cybernetics, 2019, 49(3): 1084–1096.
[6] BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender systems survey[J]. Knowledge-based systems, 2013, 46: 109–132.
[7] PAZZANI M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial intelligence review, 1999, 13(5/6): 393–408.
[8] LUCAS J P, LUZ N, MORENO M N, et al. A hybrid recommendation approach for a tourism system[J]. Expert systems with applications, 2013, 40(9): 3532–3550.
[9] LONGHI E, Titz JB, et al.“Open data: Challenges and opportunities for the tourism industry,”Tourism management, Marketing and development[J]. 2014, 57-76.
[10] SAH M, WADE V. Personalized concept-based search on the linked open data[J]. Journal of web semantics, 2016, 36: 32–57.
[11] PANTANO E, PRIPORAS C V, STYLOS N. ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction[J]. Tourism management, 2017, 60: 430–438.
[12] REN Xingyi, SONG Meina, E Haihong, et al. Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation[J]. Neurocomputing, 2017, 241: 38–55.
[13] GAO Rong, LI Jing, DU Bo, et al. Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation[J]. China communications, 2018, 15(7): 180–201.
[14] LI Xin, XU Guandong, CHEN Enhong, et al. Learning recency based comparative choice towards point-of-interest recommendation[J]. Expert systems with applications, 2015, 42(9): 4274–4283.
[15] GAO Rong, LI Jing, LI Xuefei, et al. A personalized point-of-interest recommendation model via fusion of geo-social information[J]. Neurocomputing, 2018, 273: 159–170.
[16] XU Guandong, FU Bin, GU Yanhui. Point-of-interest recommendations via a supervised random walk algorithm[J]. IEEE intelligent systems, 2016, 31(1): 15–23.
[17] GAO Rong, LI Jing, LI Xuefei, et al. STSCR: exploring spatial-temporal sequential influence and social information for location recommendation[J]. Neurocomputing, 2018, 319: 118–133.
[18] WEN Yuting, YEO J, PENG W, et al. Efficient keyword-aware representative travel route recommendation[J]. IEEE transactions on knowledge and data engineering, 2017, 29(8): 1639–1652.
[19] 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.
[20] HANG Lei, KANG S H, JIN Wenquan, et al. Design and implementation of an optimal travel route recommender system on big data for tourists in Jeju[J]. Processes, 2018, 6(8): 133.
[21] W?RNDL W, HEFELE A, HERZOG D. Recommending a sequence of interesting places for tourist trips[J]. Information technology & tourism, 2017, 17(1): 31–54.
[22] JIANG Shuhui, QIAN Xueming, MEI Tao, et al. Personalized travel sequence recommendation on multi-source big social media[J]. IEEE transactions on big data, 2016, 2(1): 43–56.
[23] CUI Ge, LUO Jun, WANG Xin. Personalized travel route recommendation using collaborative filtering based on GPS trajectories[J]. International journal of digital earth, 2018, 11(3): 284–307.
[24] DUAN Zongtao, TANG Lei, GONG Xuehui, et al. Personalized service recommendations for travel using trajectory pattern discovery[J]. International journal of distributed sensor networks, 2018, 14(3).
[25] CHEN Dawei, ONG C S, XIE Lexing. Learning points and routes to recommend trajectories[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Indianapolis, IN, USA, 2016.
[26] ZHU Liang, XU Changqiao, GUAN Jianfeng, et al. SEM-PPA: a semantical pattern and preference-aware service mining method for personalized point of interest recommendation[J]. Journal of network and computer applications, 2017, 82: 35–46.
[27] HSIEH H P, LI Chengte, LIN Shoude. Measuring and recommending time-sensitive routes from location-based data[J]. ACM transactions on intelligent systems and technology, 2014, 5(3): 45.
[28] SUN Yeran, FAN Hongchao, BAKILLAH M, et al. Road-based travel recommendation using geo-tagged images[J]. Computers, environment and urban systems, 2015, 53: 110–122.
[29] LIM K H, CHAN J, LECKIE C, et al. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency[J]. Knowledge and information systems, 2018, 54(2): 375–406.
[30] HAN J, LEE H. Adaptive landmark recommendations for travel planning: personalizing and clustering landmarks using geo-tagged social media[J]. Pervasive and mobile computing, 2015, 18: 4–17.
[31] KAUSHIK S, TIWARI S, AGARWAL C, et al. Ubiquitous crowdsourcing model for location recommender system[J]. Journal of computers, 2016, 11(6): 463–471.
[32] YU Yaxin, ZHAO Yuhai, YU Ge, et al. Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes[J]. Frontiers of computer science, 2017, 11(6): 1007–1022.
[33] WANG Xiangyu, ZHAO Yiliang, NIE Liqiang, et al. Semantic-based location recommendation with multimodal venue semantics[J]. IEEE transactions on multimedia, 2015, 17(3): 409–419.
[34] ARAIN Q A, MEMON H, MEMON I, et al. Intelligent travel information platform based on location base services to predict user travel behavior from user-generated GPS traces[J]. International journal of computers and applications, 2017, 39(3): 155–168.
[35] PáLOVICS R, SZALAI P, PAP J, et al. Location-aware online learning for top-k recommendation[J]. Pervasive and mobile computing, 2017, 38: 490–504.
[36] SMIRNOV A V, KASHEVNIK A M, PONOMAREV A. Context-based infomobility system for cultural heritage recommendation: Tourist Assistant—TAIS[J]. Personal and ubiquitous computing, 2017, 21(2): 297–311.
[37] SHI Lin, LIN Feiyu, YANG Tianchu, et al. Context-based ontology-driven recommendation strategies for tourism in ubiquitous computing[J]. Wireless personal communications, 2014, 76(4): 731–745.
[38] VOLKOVA L, YAGUNOVA E, PRONOZA E, et al. Recommender system for tourist itineraries based on aspects extraction from reviews corpora[J]. Polibits, 2018, 57: 81–88.
[39] FERRARO P, LO RE G. Designing ontology-driven recommender systems for tourism[M]//GAGLIO S, LO RE G. Advances onto the Internet of Things. Cham, Germany: Springer, 2014: 339-352.
[40] KESORN K, JURAPHANTHONG W, SALAIWARAKUL A. Personalized attraction recommendation system for tourists through check-in data[J]. IEEE access, 2017, 5: 26703–26721.
[41] GAO Xurui, WANG Li, WU Weili. Using multi-features to recommend friends on location-based social networks[J]. Peer-to-peer networking and applications, 2017, 10(6): 1323–1330.
[42] KOSMIDES P, DEMESTICHAS K P, ADAMOPOULOU E, et al. Providing recommendations on location-based social networks[J]. Journal of ambient intelligence and humanized computing, 2016, 7(4): 567–578.
[43] ZHAO Xiangguo, MA Zhongyu, ZHANG Zhen. A novel recommendation system in location-based social networks using distributed ELM[J]. Memetic computing, 2018, 10(3): 321–331.
[44] HUANG Liwei, MA Yutao, LIU Yanbo. Point-of-interest recommendation in location-based social networks with personalized geo-social influence[J]. China communications, 2015, 12(12): 21–31.
[45] KEFALAS P, SYMEONIDIS P, MANOLOPOULOS Y. Recommendations based on a heterogeneous spatio-temporal social network[J]. World wide web, 2018, 21(2): 345–371.
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[1]匡海丽,常亮,宾辰忠,等.上下文感知旅游推荐系统研究综述[J].智能系统学报,2019,14(4):611.[doi:10.11992/tis.201901013]
 KUANG Haili,CHANG Liang,BIN Chenzhong,et al.Review of a context-aware travel recommendation system[J].CAAI Transactions on Intelligent Systems,2019,14():611.[doi:10.11992/tis.201901013]

备注/Memo

收稿日期:2019-12-18。
基金项目:国家自然科学基金项目(U1711263,U1811264);广西自然科学基金项目(2018GXNSFDA281045)
作者简介:YOCHUM Phatpicha,博士研究生,主要研究方向为机器学习、推荐系统;常亮,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。主持并完成国家自然科学基金项目1项、广西省自然科学基金项目1项,发表学术论文70余篇;古天龙,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、国防预研基金项目等30余项。出版学术著作3部,发 表学术论文130余篇
通讯作者:常亮.E-mail:changl@guet.edu.cn

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