Abstract
Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the “dreaming stage”, proposes the automatic recommendation of personalised destinations based on textual reviews, i.e., a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling – to extract meaningful information from textual reviews – and Semantic Similarity – to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.
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References
1. Bachrach, Y., Ceppi, S., Kash, I.A., Key, P., Radlinski, F., Porat, E., Armstrong, M., Sharma, V.: Building a personalized tourist attraction recommender system using crowdsourcing. In: AAMAS ’14. pp. 1631–1632. International Foundation for Autonomous Agents and Multiagent Systems, Paris (2014)
2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3, 993–1022 (2003)
3. Borras, J., Moreno, A., Valls, A.: Intelligent tourism recommender systems: A survey. Expert Systems with Applications 41(16), 7370–7389 (2014)
4. Chittilappilly, A.I., Chen, L., Amer-Yahia, S.: A survey of general-purpose crowdsourcing techniques. IEEE Transactions on Knowledge and Data Engineering 28(9), 2246–2266 (2016)
5. Felfernig, A., Gordea, S., Jannach, D., Teppan, E., Zanker, M.: A short survey of recommendation technologies in travel and tourism. OEGAI Journal 25(7), 17–22 (2007)
6. Gavalas, D., Kasapakis, V., Konstantopoulos, C., Mastakas, K., Pantziou, G.: A survey on mobile tourism recommender systems. In: ICCIT 2013. pp. 131–135. IEEE, Beirut (2013)
7. Gonzalez, G., Lopez, B., De la Rosa, J.: Smart user models for tourism: A holistic approach for personaliaed tourism services. Information Technology & Tourism 6(4), 273–286 (2003)
8. Graham, S., Weingart, S., Milligan, I.: Getting started with topic modeling and mallet. The Programming Historian 2, 12 (2012)
9. Guo, T., Guo, B., Zhang, J., Yu, Z., Zhou, X.: Crowdtravel: Leveraging heterogeneous crowdsourced data for scenic spot profiling and recommendation. In: PCM 2016. LNCS, vol. 9917, pp. 617–628. Springer, Xian (2016)
10. Leal, F., Dias, J.M., Malheiro, B., Burguillo, J.C.: Analysis and visualisation of crowdsourced tourism data. In: C3S2E ’16. pp. 98–101. ACM, Porto (2016)
11. Miller, G.A.: WordNet: A lexical database for English. Commun. ACM 38(11), 39–41 (Nov 1995)
12. Patil, P., Kolhe, V.: Survey of travel package recommendation system. International Journal of Science and Research 3(12), 1557–1561 (2014)
13. Ricci, F.,Werthner, H.: Case base querying for travel planning recommendation. Information Technology & Tourism 4(3-1), 215–226 (2001)
14. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender systems handbook, pp. 257–297. Springer (2011)
15. Tiwari, S., Kaushik, S.: Crowdsourcing based fuzzy information enrichment of tourist spot recommender systems. In: ICCSA 2015. LNCS, vol. 9158, pp. 559–574. Springer, Banff (2015)
16. World Tourism Organization (UNWTO) Affiliate Members: Technology in tourism. AMreports 1, UNWTO (2011)
17. Yu, Z., Xu, H., Yang, Z., Guo, B.: Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems 46(1), 151–158 (2016)
18. Zhuang, Y., Zhuge, F., Chiu, D., Ju, C., Jiang, B.: A personalized travel system based on crowdsourcing model. In: ADMA 2014. LNCS, vol. 8933, pp. 163–174. Springer, Guilin (2014)
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Leal, F., González–Vélez, H., Malheiro, B., Burguillo, J.C. (2018). Semantic Profiling and Destination Recommendation based on Crowd-sourced Tourist Reviews. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_17
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DOI: https://doi.org/10.1007/978-3-319-62410-5_17
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