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SCIENTIA SINICA Informationis
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SCIENTIA SINICA Informationis, Volume 50 , Issue 7 : 1055-1068(2020) https://doi.org/10.1360/SSI-2019-0268

An interpretable attraction recommendation method based on knowledge graph

Jialiang GAO 1,2, Peiyuan QIU 3, Li YU 4, Zongcai HUANG 1,2, Feng LU 1,2,5,*
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  • ReceivedDec 1, 2019
  • AcceptedApr 28, 2020
  • PublishedJul 9, 2020
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Abstract


Funded by

国家自然科学基金项目(41631177,41801320)


References

[1] Ricci F, Rokach L, Shapira B. Recommender systems: introduction and challenges. In: Recommender Systems Handbook. Berlin: Springer, 2015. Google Scholar

[2] Yeh D Y, Cheng C H. Recommendation system for popular tourist attractions in Taiwan using Delphi panel and repertory grid techniques. Tourism Manage, 2015, 46: 164-176 CrossRef Google Scholar

[3] Pazzani M J, Billsus D. Content-based recommendation systems. In: Proceedings of the Adaptive Web, 2007. 325--341. Google Scholar

[4] Herlocker J L, Konstan J A, Borchers A, et al. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999. Google Scholar

[5] Elkahky A, Song Y, He X D. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, 2015. Google Scholar

[6] Shwartz-Ziv R, Tishby N. Opening the black box of deep neural networks via information. 2017,. arXiv Google Scholar

[7] Lin Y K, Liu Z Y, Sun M S, et al. Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015. Google Scholar

[8] Wang H W, Zhang F Z, Wang J L, et al. Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018. Google Scholar

[9] Petronis K R. A different kind of contextual effect: geographical clustering of cocaine incidence in the USA. J Epidemiology Community Health, 2003, 57: 893-900 CrossRef Google Scholar

[10] Soja E W. Regions in context: spatiality, periodicity, and the historical geography of the regional question. Environ Plann D, 1985, 3: 175-190 CrossRef Google Scholar

[11] Mathias M, Zhou F, Torres-Moreno J M. Personalized sightseeing tours: a model for visits in art museums. Int J Geographical Inf Sci, 2017, 31: 591-616 CrossRef Google Scholar

[12] Gras B, Brun A, Boyer A. Identifying grey sheep users in collaborative filtering: a distribution-based technique. In: Proceedings of Conference on User Modeling Adaptation and Personalization, 2016. Google Scholar

[13] Wang R X, Fu B, Fu G, et al. Deep & cross network for ad click predictions. In: Proceedings of Knowledge Discovery and Data Mining, 2017. Google Scholar

[14] Guo H F, Tang R M, Ye Y M, et al. DeepFM: a factorization-machine based neural network for CTR prediction. 2017,. arXiv Google Scholar

[15] Choi C, Cho M, Choi J, et al. Travel ontology for intelligent recommendation system. In: Proceedings of the 3rd Asia International Conference on Modelling & Simulation, 2009. Google Scholar

[16] Daramola J O, Adigun M O, Ayo C K. Building an ontology-based framework for tourism recommendation services. In: Proceedings of Information Communication Technologies in Tourism, 2009. 135--147. Google Scholar

[17] Lu C, Laublet P, Stankovic M. Travel attractions recommendation with knowledge graphs. In: Proceedings of Knowledge Acquisition, Modeling and Management, 2016. Google Scholar

[18] Sun Y Z, Han J W, Yan X F, et al, Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: Proceedings of Very Large Data Bases, 2011. 992--1003. Google Scholar

[19] Yu X, Ren X, Sun Y Z, et al. Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 2014. Google Scholar

[20] Wang Q, Mao Z, Wang B. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Trans Knowl Data Eng, 2017, 29: 2724-2743 CrossRef Google Scholar

[21] Wang H W, Zhang F Z, Zhao M, et al. Multi-task feature learning for knowledge graph enhanced recommendation. In: Proceedings of World Wide Web Conference, 2019. Google Scholar

[22] Brockmann D, Hufnagel L, Geisel T. The scaling laws of human travel. Nature, 2006, 439: 462-465 CrossRef ADS arXiv Google Scholar

[23] Cleveland R B, Cleveland W S, Mcrae J E, et al. STL: a seasonal-trend decomposition procedure based on LOESS. J Off Stat, 1990, 6: 3--33. Google Scholar

[24] Xu B, Xu Y, Liang J Q, et al. CN-DBpedia: a never-ending chinese knowledge extraction system. In: Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2017. Google Scholar