Travel Attractions Recommendation with Knowledge Graphs

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• 关键词：e ; Tourism ; Travel attraction ; Recommender system ; Semantic information ; Knowledge graph ; Ontology
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10024
• 期：1
• 页码：416-431
• 全文大小：2,080 KB
• 参考文献：1.Google: Travel trends: 4 mobile moments changing the consumer journey (2015). https://​www.​thinkwithgoogle.​com/​articles/​travel-trends-4-mobile-moments-changing-consumer-journey.​html
  2.Expedia: Custom Research: Exploring the Traveler’s Path to Purchase (2014). https://​info.​advertising.​expedia.​com/​travelerspathtop​urchase
  3.Passant, A.: dbrec — music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, Jeff, Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-17749-1_​14 CrossRef
  4.Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 1–8. ACM, September 2012
  5.Ristoski, P., Loza Mencía, E., Paulheim, H.: A hybrid multi-strategy recommender system using linked open data. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 150–156. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-12024-9_​19
  6.Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks a survey. GeoInformatica 19(3), 525–565 (2015)CrossRef
  7.Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI Conference on Artificial Intelligence, July 2012
  8.Ye, M., Yin, P., Lee, W. C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM, July 2011
  9.Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM, July 2013
  10.Braunhofer, M., Elahi, M., Ricci, F., Schievenin, T.: Context-aware points of interest suggestion with dynamic weather data management. In: Xiang, Z., Tussyadiah, I. (eds.) Information and communication technologies in tourism 2014, pp. 87–100. Springer International Publishing, Cham (2013)CrossRef
  11.Noguera, J.M., Barranco, M.J., Segura, R.J., Martínez, L.: A mobile 3D-GIS hybrid recommender system for tourism. Inf. Sci. 215, 37–52 (2012)CrossRef
  12.Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. In: SDM, vol. 13, pp. 396–404, May 2013
  13.Brilhante, I.R., Macedo, J.A., Nardini, F.M., Perego, R., Renso, C.: On planning sightseeing tours with TripBuilder. Inf. Process. Manag. 51(2), 1–15 (2015)CrossRef
  14.Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 199–208, November 2012. ACM
  15.Chen, C., Zhang, D., Guo, B., Ma, X., Pan, G., Wu, Z.: TripPlanner: personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Trans. Intell. Transp. Syst. 16(3), 1259–1273 (2015)CrossRef
  16.Lim, K.H., Chan, J., Leckie, C., Karunasekera, S.: Personalized tour recommendation based on user interests and points of interest visit durations. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015) (2015)
  17.Lu, C., Stankovic, M., Laublet, P.: Desperately searching for travel offers? Formulate better queries with some help from linked data. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 621–636. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-18818-8_​38 CrossRef
  18.Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Li, L.J.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64–73 (2016)CrossRef
  19.Di Noia, T., Cantador, I., Ostuni, V.C.: Linked open data-enabled recommender systems: ESWC 2014 challenge on book recommendation. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 129–143. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-12024-9_​17
  
• 作者单位：Chun Lu (17) (18)
  Philippe Laublet (18)
  Milan Stankovic (17) (18)
  
  17. Sépage, 27 rue du Chemin Vert, 75011, Paris, France
  18. STIH, Université Paris-Sorbonne, 28 rue Serpente, 75006, Paris, France
  
• 丛书名：Knowledge Engineering and Knowledge Management
• ISBN：978-3-319-49004-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10024

文摘

Selecting relevant travel attractions for a given user is a real and important problem from both a traveller’s and a travel supplier’s perspectives. Knowledge graphs have been used to conduct recommendations of music artists, movies and books. In this paper, we identify how knowledge graphs might be efficiently leveraged to recommend travel attractions. We improve two main drawbacks in existing systems where semantic information is exploited: semantic poorness and city-agnostic user profiling strategy. Accordingly, we constructed a rich world scale travel knowledge graph from existing large knowledge graphs namely Geonames, DBpedia and Wikidata. The underlying ontology contains more than 1200 classes to describe attractions. We applied a city-dependent user profiling strategy that makes use of the fine semantics encoded in the constructed graph. Our evaluation on YFCC100M dataset showed that our approach achieves a 5.3 % improvement in terms of F1-score, a 4.3 % improvement in terms of nDCG compared with the state-of-the-art approach.